AUTOML and model Explainability
• 270 min read
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import seaborn as sns
%matplotlib inline
pd.set_option('display.max_rows',None)
fraud_df = pd.read_csv("C:\Books\ML Project\insurance_claims.csv",index_col='policy_number')
#fraud_df
fraud_df.head()
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 521585 | 328 | 48 | 17-10-2014 | OH | 250/500 | 1000 | 1406.91 | 0 | 466132 | MALE | ... | 2 | YES | 71610 | 6510 | 13020 | 52080 | Saab | 92x | 2004 | Y |
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 687698 | 134 | 29 | 06-09-2000 | OH | 100/300 | 2000 | 1413.14 | 5000000 | 430632 | FEMALE | ... | 3 | NO | 34650 | 7700 | 3850 | 23100 | Dodge | RAM | 2007 | N |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 367455 | 228 | 44 | 06-06-2014 | IL | 500/1000 | 1000 | 1583.91 | 6000000 | 610706 | MALE | ... | 1 | NO | 6500 | 1300 | 650 | 4550 | Accura | RSX | 2009 | N |
5 rows × 38 columns
fraud_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 1000 non-null int64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 1000 non-null float64 7 umbrella_limit 1000 non-null int64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null object 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 1000 non-null int64 31 injury_claim 1000 non-null int64 32 property_claim 1000 non-null int64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(1), int64(16), object(21) memory usage: 304.7+ KB
fraud_df.fraud_reported.value_counts()
N 753 Y 247 Name: fraud_reported, dtype: int64
fraud_df.shape
(1000, 38)
fraud_df.isnull().sum()
months_as_customer 0 age 0 policy_bind_date 0 policy_state 0 policy_csl 0 policy_deductable 0 policy_annual_premium 0 umbrella_limit 0 insured_zip 0 insured_sex 0 insured_education_level 0 insured_occupation 0 insured_hobbies 0 insured_relationship 0 capital-gains 0 capital-loss 0 incident_date 0 incident_type 0 collision_type 0 incident_severity 0 authorities_contacted 0 incident_state 0 incident_city 0 incident_location 0 incident_hour_of_the_day 0 number_of_vehicles_involved 0 property_damage 0 bodily_injuries 0 witnesses 0 police_report_available 0 total_claim_amount 0 injury_claim 0 property_claim 0 vehicle_claim 0 auto_make 0 auto_model 0 auto_year 0 fraud_reported 0 dtype: int64
fraud_df.describe()
| months_as_customer | age | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | capital-gains | capital-loss | incident_hour_of_the_day | number_of_vehicles_involved | bodily_injuries | witnesses | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1.000000e+03 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 |
| mean | 203.954000 | 38.948000 | 1136.000000 | 1256.406150 | 1.101000e+06 | 501214.488000 | 25126.100000 | -26793.700000 | 11.644000 | 1.83900 | 0.992000 | 1.487000 | 52761.94000 | 7433.420000 | 7399.570000 | 37928.950000 | 2005.103000 |
| std | 115.113174 | 9.140287 | 611.864673 | 244.167395 | 2.297407e+06 | 71701.610941 | 27872.187708 | 28104.096686 | 6.951373 | 1.01888 | 0.820127 | 1.111335 | 26401.53319 | 4880.951853 | 4824.726179 | 18886.252893 | 6.015861 |
| min | 0.000000 | 19.000000 | 500.000000 | 433.330000 | -1.000000e+06 | 430104.000000 | 0.000000 | -111100.000000 | 0.000000 | 1.00000 | 0.000000 | 0.000000 | 100.00000 | 0.000000 | 0.000000 | 70.000000 | 1995.000000 |
| 25% | 115.750000 | 32.000000 | 500.000000 | 1089.607500 | 0.000000e+00 | 448404.500000 | 0.000000 | -51500.000000 | 6.000000 | 1.00000 | 0.000000 | 1.000000 | 41812.50000 | 4295.000000 | 4445.000000 | 30292.500000 | 2000.000000 |
| 50% | 199.500000 | 38.000000 | 1000.000000 | 1257.200000 | 0.000000e+00 | 466445.500000 | 0.000000 | -23250.000000 | 12.000000 | 1.00000 | 1.000000 | 1.000000 | 58055.00000 | 6775.000000 | 6750.000000 | 42100.000000 | 2005.000000 |
| 75% | 276.250000 | 44.000000 | 2000.000000 | 1415.695000 | 0.000000e+00 | 603251.000000 | 51025.000000 | 0.000000 | 17.000000 | 3.00000 | 2.000000 | 2.000000 | 70592.50000 | 11305.000000 | 10885.000000 | 50822.500000 | 2010.000000 |
| max | 479.000000 | 64.000000 | 2000.000000 | 2047.590000 | 1.000000e+07 | 620962.000000 | 100500.000000 | 0.000000 | 23.000000 | 4.00000 | 2.000000 | 3.000000 | 114920.00000 | 21450.000000 | 23670.000000 | 79560.000000 | 2015.000000 |
fraud_df.auto_year.value_counts()
1995 56 1999 55 2005 54 2011 53 2006 53 2007 52 2003 51 2010 50 2009 50 2013 49 2002 49 2015 47 1997 46 2012 46 2008 45 2014 44 2001 42 2000 42 1998 40 2004 39 1996 37 Name: auto_year, dtype: int64
fraud_df['auto_make'].value_counts()
Suburu 80 Saab 80 Dodge 80 Nissan 78 Chevrolet 76 Ford 72 BMW 72 Toyota 70 Audi 69 Volkswagen 68 Accura 68 Jeep 67 Mercedes 65 Honda 55 Name: auto_make, dtype: int64
fraud_df.auto_model.value_counts()
RAM 43 Wrangler 42 A3 37 Neon 37 MDX 36 Jetta 35 Passat 33 A5 32 Legacy 32 Pathfinder 31 Malibu 30 Forrestor 28 92x 28 Camry 28 F150 27 E400 27 95 27 Grand Cherokee 25 93 25 Tahoe 24 Escape 24 Maxima 24 X5 23 Ultima 23 Silverado 22 Civic 22 Highlander 22 Fusion 21 ML350 20 Corolla 20 CRV 20 Impreza 20 TL 20 3 Series 18 C300 18 X6 16 M5 15 Accord 13 RSX 12 Name: auto_model, dtype: int64
sns.boxplot(x=fraud_df['age'])
<AxesSubplot:xlabel='age'>
sns.pairplot(fraud_df)
<seaborn.axisgrid.PairGrid at 0x27b2834a940>
fraud_df.head(10)
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 521585 | 328 | 48 | 17-10-2014 | OH | 250/500 | 1000 | 1406.91 | 0 | 466132 | MALE | ... | 2 | YES | 71610 | 6510 | 13020 | 52080 | Saab | 92x | 2004 | Y |
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 687698 | 134 | 29 | 06-09-2000 | OH | 100/300 | 2000 | 1413.14 | 5000000 | 430632 | FEMALE | ... | 3 | NO | 34650 | 7700 | 3850 | 23100 | Dodge | RAM | 2007 | N |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 367455 | 228 | 44 | 06-06-2014 | IL | 500/1000 | 1000 | 1583.91 | 6000000 | 610706 | MALE | ... | 1 | NO | 6500 | 1300 | 650 | 4550 | Accura | RSX | 2009 | N |
| 104594 | 256 | 39 | 12-10-2006 | OH | 250/500 | 1000 | 1351.10 | 0 | 478456 | FEMALE | ... | 2 | NO | 64100 | 6410 | 6410 | 51280 | Saab | 95 | 2003 | Y |
| 413978 | 137 | 34 | 04-06-2000 | IN | 250/500 | 1000 | 1333.35 | 0 | 441716 | MALE | ... | 0 | ? | 78650 | 21450 | 7150 | 50050 | Nissan | Pathfinder | 2012 | N |
| 429027 | 165 | 37 | 03-02-1990 | IL | 100/300 | 1000 | 1137.03 | 0 | 603195 | MALE | ... | 2 | YES | 51590 | 9380 | 9380 | 32830 | Audi | A5 | 2015 | N |
| 485665 | 27 | 33 | 05-02-1997 | IL | 100/300 | 500 | 1442.99 | 0 | 601734 | FEMALE | ... | 1 | YES | 27700 | 2770 | 2770 | 22160 | Toyota | Camry | 2012 | N |
| 636550 | 212 | 42 | 25-07-2011 | IL | 100/300 | 500 | 1315.68 | 0 | 600983 | MALE | ... | 1 | ? | 42300 | 4700 | 4700 | 32900 | Saab | 92x | 1996 | N |
10 rows × 38 columns
sns.boxplot(x=fraud_df['months_as_customer'],y=fraud_df['fraud_reported'])
<AxesSubplot:xlabel='months_as_customer', ylabel='fraud_reported'>
sns.displot(fraud_df['months_as_customer'])
<seaborn.axisgrid.FacetGrid at 0x27b32ff6520>
sns.distplot(fraud_df['age'])
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
<AxesSubplot:xlabel='age', ylabel='Density'>
sns.countplot(fraud_df.auto_make)
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
<AxesSubplot:xlabel='auto_make', ylabel='count'>
sns.countplot(fraud_df.fraud_reported)
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
<AxesSubplot:xlabel='fraud_reported', ylabel='count'>
num=[]
cat=[]
for i in fraud_df.columns:
if fraud_df[i].dtype=='object':
cat.append(i)
else:
num.append(i)
print(cat)
['policy_bind_date', 'policy_state', 'policy_csl', 'insured_sex', 'insured_education_level', 'insured_occupation', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'authorities_contacted', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'police_report_available', 'auto_make', 'auto_model', 'fraud_reported']
print(num)
['months_as_customer', 'age', 'policy_deductable', 'policy_annual_premium', 'umbrella_limit', 'insured_zip', 'capital-gains', 'capital-loss', 'incident_hour_of_the_day', 'number_of_vehicles_involved', 'bodily_injuries', 'witnesses', 'total_claim_amount', 'injury_claim', 'property_claim', 'vehicle_claim', 'auto_year']
fraud_df[num].describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| months_as_customer | 1000.0 | 2.039540e+02 | 1.151132e+02 | 0.00 | 115.7500 | 199.5 | 276.250 | 479.00 |
| age | 1000.0 | 3.894800e+01 | 9.140287e+00 | 19.00 | 32.0000 | 38.0 | 44.000 | 64.00 |
| policy_deductable | 1000.0 | 1.136000e+03 | 6.118647e+02 | 500.00 | 500.0000 | 1000.0 | 2000.000 | 2000.00 |
| policy_annual_premium | 1000.0 | 1.256406e+03 | 2.441674e+02 | 433.33 | 1089.6075 | 1257.2 | 1415.695 | 2047.59 |
| umbrella_limit | 1000.0 | 1.101000e+06 | 2.297407e+06 | -1000000.00 | 0.0000 | 0.0 | 0.000 | 10000000.00 |
| insured_zip | 1000.0 | 5.012145e+05 | 7.170161e+04 | 430104.00 | 448404.5000 | 466445.5 | 603251.000 | 620962.00 |
| capital-gains | 1000.0 | 2.512610e+04 | 2.787219e+04 | 0.00 | 0.0000 | 0.0 | 51025.000 | 100500.00 |
| capital-loss | 1000.0 | -2.679370e+04 | 2.810410e+04 | -111100.00 | -51500.0000 | -23250.0 | 0.000 | 0.00 |
| incident_hour_of_the_day | 1000.0 | 1.164400e+01 | 6.951373e+00 | 0.00 | 6.0000 | 12.0 | 17.000 | 23.00 |
| number_of_vehicles_involved | 1000.0 | 1.839000e+00 | 1.018880e+00 | 1.00 | 1.0000 | 1.0 | 3.000 | 4.00 |
| bodily_injuries | 1000.0 | 9.920000e-01 | 8.201272e-01 | 0.00 | 0.0000 | 1.0 | 2.000 | 2.00 |
| witnesses | 1000.0 | 1.487000e+00 | 1.111335e+00 | 0.00 | 1.0000 | 1.0 | 2.000 | 3.00 |
| total_claim_amount | 1000.0 | 5.276194e+04 | 2.640153e+04 | 100.00 | 41812.5000 | 58055.0 | 70592.500 | 114920.00 |
| injury_claim | 1000.0 | 7.433420e+03 | 4.880952e+03 | 0.00 | 4295.0000 | 6775.0 | 11305.000 | 21450.00 |
| property_claim | 1000.0 | 7.399570e+03 | 4.824726e+03 | 0.00 | 4445.0000 | 6750.0 | 10885.000 | 23670.00 |
| vehicle_claim | 1000.0 | 3.792895e+04 | 1.888625e+04 | 70.00 | 30292.5000 | 42100.0 | 50822.500 | 79560.00 |
| auto_year | 1000.0 | 2.005103e+03 | 6.015861e+00 | 1995.00 | 2000.0000 | 2005.0 | 2010.000 | 2015.00 |
fraud_df.replace({'umbrella_limit':-1000000.00},{'umbrella_limit':0},inplace=True)
fraud_df[num].describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| months_as_customer | 1000.0 | 2.039540e+02 | 1.151132e+02 | 0.00 | 115.7500 | 199.5 | 276.250 | 479.00 |
| age | 1000.0 | 3.894800e+01 | 9.140287e+00 | 19.00 | 32.0000 | 38.0 | 44.000 | 64.00 |
| policy_deductable | 1000.0 | 1.136000e+03 | 6.118647e+02 | 500.00 | 500.0000 | 1000.0 | 2000.000 | 2000.00 |
| policy_annual_premium | 1000.0 | 1.256406e+03 | 2.441674e+02 | 433.33 | 1089.6075 | 1257.2 | 1415.695 | 2047.59 |
| umbrella_limit | 1000.0 | 1.102000e+06 | 2.296709e+06 | 0.00 | 0.0000 | 0.0 | 0.000 | 10000000.00 |
| insured_zip | 1000.0 | 5.012145e+05 | 7.170161e+04 | 430104.00 | 448404.5000 | 466445.5 | 603251.000 | 620962.00 |
| capital-gains | 1000.0 | 2.512610e+04 | 2.787219e+04 | 0.00 | 0.0000 | 0.0 | 51025.000 | 100500.00 |
| capital-loss | 1000.0 | -2.679370e+04 | 2.810410e+04 | -111100.00 | -51500.0000 | -23250.0 | 0.000 | 0.00 |
| incident_hour_of_the_day | 1000.0 | 1.164400e+01 | 6.951373e+00 | 0.00 | 6.0000 | 12.0 | 17.000 | 23.00 |
| number_of_vehicles_involved | 1000.0 | 1.839000e+00 | 1.018880e+00 | 1.00 | 1.0000 | 1.0 | 3.000 | 4.00 |
| bodily_injuries | 1000.0 | 9.920000e-01 | 8.201272e-01 | 0.00 | 0.0000 | 1.0 | 2.000 | 2.00 |
| witnesses | 1000.0 | 1.487000e+00 | 1.111335e+00 | 0.00 | 1.0000 | 1.0 | 2.000 | 3.00 |
| total_claim_amount | 1000.0 | 5.276194e+04 | 2.640153e+04 | 100.00 | 41812.5000 | 58055.0 | 70592.500 | 114920.00 |
| injury_claim | 1000.0 | 7.433420e+03 | 4.880952e+03 | 0.00 | 4295.0000 | 6775.0 | 11305.000 | 21450.00 |
| property_claim | 1000.0 | 7.399570e+03 | 4.824726e+03 | 0.00 | 4445.0000 | 6750.0 | 10885.000 | 23670.00 |
| vehicle_claim | 1000.0 | 3.792895e+04 | 1.888625e+04 | 70.00 | 30292.5000 | 42100.0 | 50822.500 | 79560.00 |
| auto_year | 1000.0 | 2.005103e+03 | 6.015861e+00 | 1995.00 | 2000.0000 | 2005.0 | 2010.000 | 2015.00 |
fraud_df[cat].describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| policy_bind_date | 1000 | 951 | 01-01-2006 | 3 |
| policy_state | 1000 | 3 | OH | 352 |
| policy_csl | 1000 | 3 | 250/500 | 351 |
| insured_sex | 1000 | 2 | FEMALE | 537 |
| insured_education_level | 1000 | 7 | JD | 161 |
| insured_occupation | 1000 | 14 | machine-op-inspct | 93 |
| insured_hobbies | 1000 | 20 | reading | 64 |
| insured_relationship | 1000 | 6 | own-child | 183 |
| incident_date | 1000 | 60 | 02-02-2015 | 28 |
| incident_type | 1000 | 4 | Multi-vehicle Collision | 419 |
| collision_type | 1000 | 4 | Rear Collision | 292 |
| incident_severity | 1000 | 4 | Minor Damage | 354 |
| authorities_contacted | 1000 | 5 | Police | 292 |
| incident_state | 1000 | 7 | NY | 262 |
| incident_city | 1000 | 7 | Springfield | 157 |
| incident_location | 1000 | 1000 | 9043 Maple Hwy | 1 |
| property_damage | 1000 | 3 | ? | 360 |
| police_report_available | 1000 | 3 | NO | 343 |
| auto_make | 1000 | 14 | Suburu | 80 |
| auto_model | 1000 | 39 | RAM | 43 |
| fraud_reported | 1000 | 2 | N | 753 |
fraud_df.loc[fraud_df['property_damage'] == '?']
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 413978 | 137 | 34 | 04-06-2000 | IN | 250/500 | 1000 | 1333.35 | 0 | 441716 | MALE | ... | 0 | ? | 78650 | 21450 | 7150 | 50050 | Nissan | Pathfinder | 2012 | N |
| 429027 | 165 | 37 | 03-02-1990 | IL | 100/300 | 1000 | 1137.03 | 0 | 603195 | MALE | ... | 2 | YES | 51590 | 9380 | 9380 | 32830 | Audi | A5 | 2015 | N |
| 558938 | 70 | 26 | 08-06-2005 | OH | 500/1000 | 1000 | 1199.44 | 5000000 | 619884 | MALE | ... | 2 | YES | 52110 | 5790 | 5790 | 40530 | Nissan | Maxima | 2012 | N |
| 143972 | 196 | 39 | 02-08-1992 | IN | 500/1000 | 2000 | 1475.73 | 0 | 477670 | FEMALE | ... | 0 | NO | 60400 | 6040 | 6040 | 48320 | Nissan | Pathfinder | 2014 | N |
| 431876 | 217 | 41 | 27-11-2005 | IL | 500/1000 | 2000 | 875.15 | 0 | 442479 | FEMALE | ... | 2 | ? | 37840 | 0 | 4730 | 33110 | Accura | RSX | 1996 | N |
| 115399 | 413 | 55 | 08-02-1991 | IN | 100/300 | 2000 | 1268.79 | 0 | 453148 | MALE | ... | 2 | ? | 98160 | 8180 | 16360 | 73620 | Dodge | RAM | 2011 | Y |
| 200152 | 62 | 28 | 09-03-2003 | IL | 100/300 | 1000 | 988.45 | 0 | 430141 | FEMALE | ... | 1 | YES | 60200 | 6020 | 6020 | 48160 | Suburu | Forrestor | 2004 | Y |
| 485664 | 431 | 54 | 25-11-2002 | IN | 500/1000 | 2000 | 1155.55 | 0 | 615901 | FEMALE | ... | 0 | ? | 62300 | 12460 | 6230 | 43610 | Jeep | Wrangler | 2007 | N |
| 982871 | 199 | 37 | 27-07-1997 | IN | 250/500 | 500 | 1262.08 | 0 | 474615 | MALE | ... | 3 | NO | 60170 | 10940 | 10940 | 38290 | Nissan | Pathfinder | 2011 | Y |
| 616337 | 116 | 34 | 30-08-2012 | IN | 250/500 | 500 | 1737.66 | 0 | 470577 | MALE | ... | 1 | ? | 97080 | 16180 | 16180 | 64720 | BMW | X5 | 2001 | Y |
| 866931 | 175 | 34 | 07-01-2008 | IN | 500/1000 | 1000 | 1123.87 | 8000000 | 446326 | FEMALE | ... | 0 | YES | 7290 | 810 | 810 | 5670 | Volkswagen | Passat | 1995 | N |
| 691189 | 430 | 59 | 10-01-2004 | OH | 250/500 | 2000 | 1326.62 | 7000000 | 477310 | MALE | ... | 3 | YES | 81800 | 16360 | 8180 | 57260 | Nissan | Pathfinder | 1998 | N |
| 537546 | 91 | 27 | 20-08-1994 | IL | 100/300 | 2000 | 1073.83 | 0 | 609930 | FEMALE | ... | 2 | ? | 7260 | 1320 | 660 | 5280 | BMW | M5 | 2008 | N |
| 394975 | 217 | 39 | 02-06-2002 | IN | 100/300 | 1000 | 1530.52 | 0 | 603993 | MALE | ... | 1 | YES | 4300 | 430 | 430 | 3440 | Toyota | Corolla | 2000 | N |
| 524836 | 439 | 56 | 20-11-2008 | IN | 250/500 | 500 | 1082.49 | 0 | 606714 | FEMALE | ... | 3 | ? | 56430 | 0 | 6270 | 50160 | Honda | CRV | 2014 | N |
| 486676 | 271 | 42 | 15-08-2011 | OH | 100/300 | 500 | 1105.49 | 0 | 463181 | FEMALE | ... | 3 | ? | 68310 | 12420 | 6210 | 49680 | Audi | A3 | 2003 | Y |
| 279422 | 227 | 38 | 27-10-2013 | OH | 500/1000 | 500 | 976.67 | 0 | 471600 | FEMALE | ... | 2 | ? | 72200 | 14440 | 7220 | 50540 | BMW | M5 | 2013 | Y |
| 645258 | 244 | 40 | 04-07-1997 | OH | 500/1000 | 2000 | 1267.81 | 5000000 | 603123 | FEMALE | ... | 1 | ? | 6600 | 660 | 1320 | 4620 | Accura | TL | 2005 | N |
| 498140 | 284 | 48 | 15-05-1997 | IN | 500/1000 | 2000 | 769.95 | 0 | 605486 | MALE | ... | 3 | NO | 60940 | 5540 | 11080 | 44320 | Audi | A3 | 2013 | Y |
| 614763 | 134 | 32 | 02-01-1991 | IL | 500/1000 | 500 | 1612.43 | 0 | 456762 | FEMALE | ... | 1 | YES | 64240 | 11680 | 11680 | 40880 | BMW | 3 Series | 2015 | N |
| 740019 | 295 | 48 | 17-06-2009 | OH | 250/500 | 1000 | 1148.73 | 0 | 439787 | FEMALE | ... | 2 | YES | 5400 | 900 | 900 | 3600 | Saab | 95 | 1999 | N |
| 389238 | 279 | 41 | 06-06-2001 | IL | 250/500 | 500 | 1497.35 | 0 | 460742 | FEMALE | ... | 3 | NO | 28800 | 0 | 3600 | 25200 | Ford | Fusion | 2013 | N |
| 632627 | 464 | 61 | 07-10-1990 | OH | 500/1000 | 1000 | 1125.37 | 0 | 604450 | FEMALE | ... | 2 | YES | 79800 | 6650 | 19950 | 53200 | Saab | 95 | 2000 | Y |
| 163161 | 298 | 47 | 11-11-1998 | IL | 500/1000 | 2000 | 1338.50 | 0 | 618929 | FEMALE | ... | 0 | ? | 70590 | 10860 | 10860 | 48870 | Saab | 93 | 2000 | Y |
| 776860 | 261 | 42 | 11-01-2009 | OH | 250/500 | 500 | 1337.56 | 0 | 605141 | FEMALE | ... | 2 | YES | 74700 | 7470 | 14940 | 52290 | Dodge | RAM | 2010 | N |
| 395269 | 210 | 41 | 02-11-2012 | IL | 500/1000 | 500 | 1222.75 | 0 | 432781 | MALE | ... | 0 | NO | 81070 | 14740 | 14740 | 51590 | BMW | X5 | 2001 | N |
| 981123 | 168 | 32 | 04-05-2000 | IN | 100/300 | 1000 | 1059.52 | 0 | 452748 | MALE | ... | 1 | ? | 57720 | 14430 | 9620 | 33670 | Saab | 93 | 2007 | N |
| 330591 | 225 | 41 | 05-08-1993 | OH | 500/1000 | 2000 | 1103.58 | 0 | 475292 | MALE | ... | 2 | NO | 70400 | 6400 | 12800 | 51200 | Toyota | Highlander | 2011 | Y |
| 531640 | 245 | 39 | 21-04-2001 | OH | 250/500 | 500 | 964.79 | 8000000 | 460675 | FEMALE | ... | 1 | ? | 72820 | 13240 | 6620 | 52960 | BMW | 3 Series | 2010 | N |
| 155724 | 203 | 38 | 20-02-1998 | IL | 250/500 | 500 | 1394.43 | 0 | 606352 | FEMALE | ... | 1 | YES | 55440 | 0 | 6160 | 49280 | Nissan | Maxima | 1999 | Y |
| 428230 | 165 | 32 | 04-06-2012 | IN | 500/1000 | 500 | 1399.26 | 0 | 611586 | FEMALE | ... | 0 | ? | 3960 | 330 | 660 | 2970 | BMW | M5 | 1998 | N |
| 469874 | 81 | 28 | 17-09-2011 | IL | 250/500 | 1000 | 1139.00 | 6000000 | 617858 | FEMALE | ... | 2 | ? | 44730 | 4970 | 4970 | 34790 | Saab | 92x | 2000 | Y |
| 620215 | 194 | 39 | 27-07-2005 | IN | 250/500 | 500 | 823.17 | 0 | 455689 | MALE | ... | 2 | NO | 61500 | 6150 | 12300 | 43050 | Dodge | RAM | 2012 | N |
| 618659 | 112 | 27 | 18-10-2005 | OH | 100/300 | 500 | 965.13 | 0 | 450341 | FEMALE | ... | 1 | ? | 51000 | 8500 | 8500 | 34000 | Nissan | Pathfinder | 2013 | N |
| 649082 | 24 | 33 | 19-01-1996 | IL | 500/1000 | 1000 | 1922.84 | 0 | 431277 | FEMALE | ... | 1 | NO | 46800 | 4680 | 9360 | 32760 | Jeep | Wrangler | 2002 | N |
| 437573 | 93 | 32 | 29-09-2005 | OH | 250/500 | 2000 | 1624.82 | 0 | 454656 | MALE | ... | 1 | ? | 78120 | 17360 | 8680 | 52080 | BMW | 3 Series | 2006 | N |
| 932502 | 200 | 40 | 11-05-2010 | IL | 100/300 | 1000 | 1439.34 | 0 | 444822 | FEMALE | ... | 0 | NO | 3690 | 410 | 410 | 2870 | Ford | Escape | 2015 | N |
| 935277 | 325 | 46 | 09-07-2013 | IL | 500/1000 | 500 | 1348.83 | 0 | 474360 | FEMALE | ... | 2 | YES | 76120 | 13840 | 6920 | 55360 | Toyota | Camry | 1999 | N |
| 456604 | 287 | 41 | 29-03-2004 | IL | 500/1000 | 2000 | 968.74 | 0 | 477519 | MALE | ... | 3 | ? | 5170 | 470 | 940 | 3760 | Suburu | Forrestor | 2001 | N |
| 139872 | 122 | 34 | 01-06-2006 | IN | 250/500 | 1000 | 1220.71 | 0 | 603639 | MALE | ... | 1 | YES | 8190 | 1890 | 1260 | 5040 | Chevrolet | Silverado | 2013 | N |
| 354105 | 22 | 29 | 08-06-1994 | IN | 250/500 | 2000 | 1238.62 | 6000000 | 463993 | MALE | ... | 3 | ? | 70800 | 7080 | 14160 | 49560 | Accura | MDX | 2012 | Y |
| 165485 | 106 | 31 | 12-02-1998 | IL | 500/1000 | 2000 | 1320.75 | 0 | 441491 | FEMALE | ... | 3 | YES | 45630 | 5070 | 5070 | 35490 | Accura | MDX | 2011 | N |
| 795686 | 214 | 41 | 24-10-2004 | IL | 500/1000 | 500 | 1398.51 | 4000000 | 472214 | MALE | ... | 0 | NO | 64000 | 12800 | 6400 | 44800 | BMW | X6 | 1996 | Y |
| 395983 | 209 | 38 | 08-11-2009 | OH | 100/300 | 500 | 1355.08 | 0 | 614945 | FEMALE | ... | 1 | ? | 47300 | 4730 | 4730 | 37840 | Nissan | Pathfinder | 2014 | N |
| 217938 | 193 | 41 | 16-07-1995 | OH | 250/500 | 500 | 847.03 | 0 | 438555 | FEMALE | ... | 0 | YES | 112320 | 17280 | 17280 | 77760 | Suburu | Impreza | 2011 | Y |
| 203914 | 134 | 32 | 09-06-2001 | OH | 100/300 | 1000 | 1000.06 | 0 | 440961 | FEMALE | ... | 0 | ? | 82720 | 7520 | 15040 | 60160 | Audi | A3 | 2014 | N |
| 565157 | 288 | 45 | 06-10-2002 | IL | 100/300 | 1000 | 1046.71 | 0 | 616714 | MALE | ... | 1 | ? | 48060 | 10680 | 5340 | 32040 | Mercedes | C300 | 2009 | N |
| 781181 | 461 | 61 | 27-06-2005 | OH | 100/300 | 2000 | 1402.75 | 0 | 449557 | MALE | ... | 1 | YES | 72100 | 7210 | 14420 | 50470 | Jeep | Wrangler | 2006 | N |
| 299796 | 428 | 59 | 29-09-1999 | IN | 250/500 | 500 | 1344.36 | 7000000 | 473329 | FEMALE | ... | 3 | NO | 6500 | 1300 | 650 | 4550 | Saab | 92x | 2013 | N |
| 589749 | 45 | 38 | 14-05-2006 | IN | 100/300 | 1000 | 1197.71 | 0 | 470117 | MALE | ... | 0 | ? | 78240 | 13040 | 13040 | 52160 | Suburu | Legacy | 2013 | N |
| 854021 | 136 | 29 | 29-04-2010 | OH | 100/300 | 500 | 1203.24 | 0 | 600702 | FEMALE | ... | 0 | ? | 6200 | 1240 | 620 | 4340 | Honda | Accord | 1999 | N |
| 139484 | 278 | 48 | 24-07-1999 | IN | 500/1000 | 2000 | 1142.62 | 7000000 | 475588 | FEMALE | ... | 0 | ? | 76050 | 11700 | 11700 | 52650 | Chevrolet | Silverado | 1997 | N |
| 335780 | 14 | 28 | 22-07-2002 | OH | 250/500 | 2000 | 1587.96 | 0 | 601617 | FEMALE | ... | 1 | YES | 71280 | 12960 | 12960 | 45360 | Audi | A5 | 2012 | Y |
| 140880 | 276 | 46 | 29-03-2005 | IL | 250/500 | 500 | 1448.84 | 0 | 430987 | FEMALE | ... | 1 | NO | 5940 | 660 | 660 | 4620 | Toyota | Highlander | 2015 | N |
| 288580 | 119 | 28 | 22-11-2012 | OH | 250/500 | 2000 | 1079.92 | 0 | 430886 | MALE | ... | 1 | YES | 53600 | 6700 | 6700 | 40200 | Volkswagen | Jetta | 2007 | Y |
| 425973 | 8 | 31 | 11-02-2003 | IN | 250/500 | 500 | 1229.16 | 4000000 | 604804 | FEMALE | ... | 2 | YES | 48100 | 11100 | 7400 | 29600 | Volkswagen | Jetta | 2014 | N |
| 648509 | 324 | 46 | 06-03-2010 | IN | 100/300 | 2000 | 897.89 | 6000000 | 618862 | MALE | ... | 0 | YES | 79600 | 15920 | 15920 | 47760 | Jeep | Wrangler | 2011 | N |
| 651861 | 235 | 39 | 07-01-2011 | IL | 100/300 | 500 | 1046.58 | 4000000 | 434982 | MALE | ... | 1 | NO | 4950 | 450 | 900 | 3600 | Chevrolet | Silverado | 2010 | N |
| 125324 | 53 | 36 | 13-09-2003 | OH | 500/1000 | 2000 | 1712.68 | 0 | 614233 | MALE | ... | 0 | YES | 51100 | 10220 | 5110 | 35770 | Audi | A3 | 2006 | N |
| 391652 | 86 | 26 | 12-10-1998 | OH | 100/300 | 500 | 1382.88 | 7000000 | 434923 | MALE | ... | 1 | YES | 81840 | 14880 | 7440 | 59520 | Chevrolet | Malibu | 2011 | Y |
| 922167 | 296 | 46 | 23-02-1993 | OH | 100/300 | 1000 | 1141.35 | 7000000 | 476456 | MALE | ... | 2 | NO | 54900 | 5490 | 5490 | 43920 | Mercedes | C300 | 2013 | N |
| 226330 | 177 | 34 | 23-01-2013 | IL | 100/300 | 2000 | 1057.77 | 0 | 477382 | FEMALE | ... | 1 | NO | 18000 | 2250 | 2250 | 13500 | Audi | A3 | 2009 | N |
| 524230 | 81 | 25 | 23-02-2014 | IN | 100/300 | 500 | 920.30 | 5000000 | 461958 | FEMALE | ... | 0 | ? | 73920 | 13440 | 6720 | 53760 | Honda | Civic | 2003 | Y |
| 868283 | 252 | 39 | 06-02-2006 | IN | 250/500 | 1000 | 1086.21 | 0 | 455340 | MALE | ... | 2 | ? | 50490 | 5610 | 5610 | 39270 | Nissan | Maxima | 2001 | N |
| 918777 | 73 | 26 | 04-04-2003 | IL | 250/500 | 2000 | 1191.19 | 4000000 | 468813 | MALE | ... | 1 | YES | 40160 | 5020 | 0 | 35140 | Chevrolet | Tahoe | 2003 | N |
| 602410 | 196 | 36 | 16-01-1996 | IN | 250/500 | 2000 | 1463.07 | 0 | 615611 | MALE | ... | 1 | NO | 5300 | 530 | 530 | 4240 | Jeep | Grand Cherokee | 2001 | Y |
| 171254 | 285 | 43 | 07-11-1994 | OH | 100/300 | 2000 | 1512.58 | 0 | 452496 | FEMALE | ... | 1 | ? | 2520 | 280 | 280 | 1960 | BMW | 3 Series | 1997 | N |
| 505969 | 342 | 49 | 07-04-1998 | OH | 250/500 | 500 | 1722.95 | 0 | 472634 | MALE | ... | 0 | YES | 76700 | 7670 | 7670 | 61360 | Suburu | Legacy | 2006 | N |
| 547744 | 128 | 32 | 08-07-2001 | OH | 100/300 | 2000 | 768.91 | 0 | 443522 | FEMALE | ... | 0 | NO | 59800 | 5980 | 5980 | 47840 | Ford | F150 | 1999 | Y |
| 598124 | 124 | 29 | 20-09-1993 | OH | 500/1000 | 500 | 1301.72 | 0 | 441726 | MALE | ... | 3 | YES | 72000 | 7200 | 7200 | 57600 | Audi | A3 | 2005 | N |
| 218684 | 210 | 37 | 05-08-2006 | IN | 500/1000 | 2000 | 1048.46 | 0 | 466676 | MALE | ... | 2 | ? | 7080 | 1180 | 590 | 5310 | Dodge | RAM | 1999 | N |
| 743163 | 11 | 40 | 09-04-2001 | OH | 500/1000 | 2000 | 1217.69 | 0 | 440106 | FEMALE | ... | 0 | ? | 53460 | 9720 | 9720 | 34020 | Dodge | Neon | 2004 | Y |
| 509928 | 272 | 43 | 25-07-1995 | OH | 100/300 | 1000 | 1279.13 | 0 | 615226 | MALE | ... | 2 | YES | 81070 | 7370 | 14740 | 58960 | Audi | A3 | 2006 | Y |
| 970607 | 38 | 28 | 28-03-1995 | OH | 250/500 | 1000 | 1019.44 | 0 | 437387 | MALE | ... | 1 | NO | 7200 | 1440 | 720 | 5040 | BMW | X5 | 2004 | N |
| 174701 | 328 | 46 | 19-06-1996 | IL | 500/1000 | 500 | 1314.60 | 0 | 458139 | FEMALE | ... | 3 | ? | 70290 | 12780 | 6390 | 51120 | Saab | 92x | 1998 | Y |
| 114839 | 362 | 50 | 01-01-2006 | IL | 250/500 | 500 | 1198.34 | 4000000 | 619735 | MALE | ... | 1 | NO | 57060 | 6340 | 6340 | 44380 | Mercedes | E400 | 1995 | N |
| 872734 | 241 | 38 | 19-05-1990 | IN | 100/300 | 2000 | 1003.23 | 0 | 470485 | FEMALE | ... | 3 | YES | 77880 | 12980 | 12980 | 51920 | Accura | MDX | 2008 | N |
| 629663 | 343 | 52 | 21-01-2002 | IL | 500/1000 | 1000 | 1053.02 | 0 | 602402 | FEMALE | ... | 2 | NO | 47630 | 12990 | 4330 | 30310 | Toyota | Corolla | 2005 | Y |
| 241562 | 154 | 37 | 28-01-2010 | IL | 250/500 | 1000 | 2047.59 | 0 | 439269 | FEMALE | ... | 3 | NO | 79530 | 14460 | 7230 | 57840 | Accura | MDX | 2000 | N |
| 511621 | 235 | 42 | 22-09-1990 | IN | 250/500 | 500 | 1072.62 | 0 | 444913 | FEMALE | ... | 1 | YES | 69100 | 6910 | 6910 | 55280 | Toyota | Highlander | 2006 | N |
| 476923 | 172 | 35 | 19-09-2004 | IL | 100/300 | 2000 | 1219.04 | 0 | 456602 | MALE | ... | 0 | NO | 79750 | 14500 | 14500 | 50750 | Nissan | Pathfinder | 1999 | N |
| 735822 | 27 | 28 | 28-08-1995 | IN | 100/300 | 2000 | 1371.78 | 0 | 451560 | MALE | ... | 2 | YES | 53600 | 5360 | 10720 | 37520 | Audi | A5 | 2015 | Y |
| 280709 | 272 | 41 | 06-05-1991 | OH | 500/1000 | 2000 | 1608.34 | 0 | 466718 | FEMALE | ... | 1 | ? | 71060 | 6460 | 12920 | 51680 | Saab | 92x | 2010 | N |
| 547802 | 249 | 43 | 03-09-2013 | IL | 250/500 | 1000 | 1518.46 | 0 | 606238 | FEMALE | ... | 0 | YES | 53500 | 5350 | 5350 | 42800 | Saab | 92x | 2015 | N |
| 390381 | 190 | 40 | 27-01-2007 | OH | 500/1000 | 2000 | 965.21 | 0 | 610354 | FEMALE | ... | 1 | YES | 6300 | 630 | 630 | 5040 | Nissan | Ultima | 2001 | N |
| 629918 | 174 | 36 | 14-10-2005 | IL | 100/300 | 2000 | 1278.75 | 0 | 461328 | FEMALE | ... | 2 | NO | 65400 | 10900 | 10900 | 43600 | Dodge | RAM | 2012 | N |
| 187775 | 269 | 44 | 21-12-2002 | OH | 100/300 | 500 | 1297.75 | 4000000 | 451280 | FEMALE | ... | 1 | ? | 98670 | 15180 | 15180 | 68310 | Chevrolet | Tahoe | 2010 | Y |
| 326322 | 101 | 27 | 10-02-2007 | IL | 250/500 | 1000 | 433.33 | 0 | 603269 | MALE | ... | 3 | NO | 5900 | 1180 | 590 | 4130 | Mercedes | E400 | 2009 | N |
| 146138 | 94 | 30 | 01-03-2002 | IN | 250/500 | 2000 | 1025.54 | 0 | 442632 | FEMALE | ... | 3 | YES | 64100 | 6410 | 6410 | 51280 | Chevrolet | Malibu | 2001 | N |
| 212674 | 440 | 61 | 01-09-1992 | OH | 250/500 | 500 | 1050.76 | 0 | 467942 | MALE | ... | 3 | NO | 53640 | 5960 | 5960 | 41720 | Nissan | Maxima | 2004 | Y |
| 843227 | 134 | 30 | 28-09-2007 | OH | 250/500 | 2000 | 1443.32 | 0 | 613287 | FEMALE | ... | 2 | YES | 47790 | 5310 | 5310 | 37170 | Suburu | Impreza | 1995 | Y |
| 283925 | 122 | 29 | 21-11-1991 | OH | 250/500 | 1000 | 1629.94 | 0 | 620104 | FEMALE | ... | 1 | NO | 3850 | 350 | 350 | 3150 | Dodge | Neon | 2014 | N |
| 475588 | 156 | 31 | 21-09-1996 | IL | 100/300 | 2000 | 1134.08 | 0 | 446895 | MALE | ... | 0 | ? | 59000 | 5900 | 5900 | 47200 | Ford | Fusion | 2013 | Y |
| 226725 | 244 | 40 | 11-08-1999 | IN | 500/1000 | 2000 | 1304.67 | 7000000 | 605408 | MALE | ... | 1 | ? | 61490 | 5590 | 11180 | 44720 | Dodge | RAM | 2001 | N |
| 889883 | 246 | 45 | 03-02-1999 | IL | 250/500 | 1000 | 1665.45 | 0 | 445853 | MALE | ... | 2 | ? | 53280 | 11840 | 5920 | 35520 | Toyota | Camry | 2006 | Y |
| 577810 | 369 | 55 | 15-04-2013 | OH | 250/500 | 2000 | 1589.54 | 0 | 444734 | MALE | ... | 0 | YES | 85300 | 17060 | 8530 | 59710 | Toyota | Highlander | 2003 | N |
| 727792 | 107 | 26 | 19-05-2014 | OH | 100/300 | 500 | 974.59 | 0 | 466838 | FEMALE | ... | 0 | NO | 59700 | 11940 | 11940 | 35820 | Nissan | Ultima | 2002 | N |
| 367595 | 243 | 43 | 03-02-2006 | IN | 500/1000 | 500 | 1307.74 | 0 | 466137 | FEMALE | ... | 1 | NO | 37530 | 4170 | 4170 | 29190 | Jeep | Wrangler | 2008 | N |
| 512813 | 108 | 33 | 27-01-1990 | IL | 100/300 | 2000 | 694.45 | 0 | 450703 | FEMALE | ... | 1 | YES | 64350 | 5850 | 11700 | 46800 | Nissan | Pathfinder | 2011 | Y |
| 987905 | 436 | 58 | 30-04-2002 | OH | 250/500 | 2000 | 1407.01 | 5000000 | 475705 | MALE | ... | 2 | ? | 59900 | 11980 | 5990 | 41930 | BMW | X5 | 1997 | Y |
| 967756 | 189 | 36 | 28-04-2007 | OH | 250/500 | 2000 | 1388.58 | 0 | 459122 | FEMALE | ... | 3 | YES | 79560 | 13260 | 13260 | 53040 | Ford | Escape | 2009 | N |
| 786957 | 219 | 40 | 29-10-2006 | OH | 100/300 | 500 | 1134.91 | 0 | 452735 | FEMALE | ... | 0 | NO | 6400 | 640 | 640 | 5120 | Toyota | Camry | 1997 | N |
| 444422 | 59 | 40 | 28-09-2011 | IL | 250/500 | 2000 | 782.23 | 0 | 449221 | MALE | ... | 2 | NO | 51570 | 5730 | 11460 | 34380 | BMW | X5 | 2010 | N |
| 689500 | 39 | 31 | 28-01-2003 | IL | 250/500 | 2000 | 1366.90 | 0 | 459322 | FEMALE | ... | 0 | NO | 52700 | 10540 | 10540 | 31620 | BMW | X6 | 2014 | Y |
| 384618 | 156 | 37 | 09-02-1993 | IN | 250/500 | 500 | 1090.65 | 0 | 608331 | MALE | ... | 2 | YES | 53400 | 5340 | 5340 | 42720 | Honda | Civic | 2010 | Y |
| 756459 | 123 | 31 | 05-08-2005 | IN | 250/500 | 500 | 1326.00 | 0 | 438546 | FEMALE | ... | 2 | NO | 72120 | 12020 | 12020 | 48080 | Mercedes | ML350 | 2009 | N |
| 419954 | 247 | 39 | 07-12-1993 | IL | 100/300 | 500 | 806.31 | 0 | 602177 | FEMALE | ... | 3 | ? | 3300 | 600 | 0 | 2700 | Dodge | RAM | 2003 | N |
| 426708 | 267 | 40 | 09-10-2009 | IL | 250/500 | 500 | 1155.53 | 5000000 | 465158 | MALE | ... | 2 | NO | 5670 | 1260 | 630 | 3780 | Ford | F150 | 1997 | N |
| 771236 | 175 | 34 | 29-05-1995 | OH | 100/300 | 500 | 915.29 | 0 | 607893 | FEMALE | ... | 1 | YES | 36720 | 4080 | 4080 | 28560 | Honda | Accord | 2009 | Y |
| 235869 | 111 | 29 | 22-01-2011 | IL | 250/500 | 500 | 1239.55 | 2000000 | 464736 | FEMALE | ... | 1 | YES | 52800 | 4800 | 9600 | 38400 | Mercedes | ML350 | 1996 | Y |
| 371635 | 156 | 37 | 13-10-1991 | OH | 500/1000 | 1000 | 1086.48 | 6000000 | 444903 | MALE | ... | 1 | YES | 77440 | 14080 | 7040 | 56320 | Suburu | Legacy | 1999 | N |
| 582973 | 10 | 26 | 11-06-2008 | IN | 100/300 | 2000 | 765.64 | 0 | 466191 | MALE | ... | 3 | ? | 31350 | 2850 | 5700 | 22800 | Honda | Accord | 2001 | Y |
| 278091 | 158 | 33 | 04-12-2013 | OH | 100/300 | 2000 | 1327.41 | 0 | 440930 | FEMALE | ... | 0 | ? | 35000 | 3500 | 7000 | 24500 | Suburu | Legacy | 2012 | N |
| 153154 | 436 | 59 | 21-08-2010 | OH | 500/1000 | 1000 | 1338.55 | 0 | 430380 | MALE | ... | 2 | NO | 68000 | 13600 | 6800 | 47600 | Toyota | Corolla | 2014 | N |
| 162004 | 1 | 33 | 19-09-1995 | IL | 250/500 | 500 | 903.32 | 0 | 451184 | FEMALE | ... | 0 | ? | 31700 | 6340 | 3170 | 22190 | Toyota | Highlander | 2006 | N |
| 576723 | 266 | 44 | 07-12-1999 | IL | 250/500 | 500 | 1611.83 | 0 | 460176 | MALE | ... | 2 | YES | 32800 | 3280 | 3280 | 26240 | BMW | 3 Series | 2012 | N |
| 414913 | 257 | 40 | 17-07-2012 | IN | 250/500 | 500 | 1379.93 | 0 | 608228 | MALE | ... | 2 | YES | 51810 | 9420 | 4710 | 37680 | Audi | A3 | 2002 | Y |
| 818413 | 129 | 28 | 23-02-1990 | OH | 500/1000 | 1000 | 1377.94 | 0 | 442540 | MALE | ... | 3 | ? | 44640 | 9920 | 4960 | 29760 | Toyota | Camry | 2005 | N |
| 487356 | 283 | 46 | 30-08-2000 | IL | 500/1000 | 2000 | 1313.33 | 0 | 455332 | FEMALE | ... | 3 | NO | 77660 | 7060 | 14120 | 56480 | Nissan | Maxima | 2004 | Y |
| 865839 | 101 | 26 | 02-08-1991 | IL | 500/1000 | 1000 | 1371.88 | 0 | 462420 | FEMALE | ... | 2 | ? | 3190 | 580 | 580 | 2030 | Suburu | Legacy | 1995 | N |
| 406567 | 96 | 30 | 25-09-2001 | OH | 100/300 | 500 | 1399.27 | 6000000 | 448913 | MALE | ... | 0 | YES | 53440 | 0 | 6680 | 46760 | Ford | Escape | 2004 | N |
| 563878 | 120 | 30 | 16-07-2002 | IN | 250/500 | 500 | 956.69 | 0 | 438237 | FEMALE | ... | 1 | YES | 87100 | 8710 | 8710 | 69680 | Saab | 92x | 2000 | N |
| 620855 | 212 | 35 | 29-04-1990 | IN | 500/1000 | 2000 | 1123.89 | 0 | 468313 | MALE | ... | 3 | ? | 50380 | 4580 | 4580 | 41220 | Suburu | Forrestor | 1996 | N |
| 445973 | 66 | 26 | 13-11-1998 | IL | 250/500 | 1000 | 988.29 | 0 | 476502 | MALE | ... | 2 | YES | 57860 | 0 | 10520 | 47340 | Suburu | Impreza | 2008 | Y |
| 156694 | 334 | 47 | 24-05-2001 | IL | 500/1000 | 500 | 1238.89 | 0 | 600561 | MALE | ... | 3 | NO | 6240 | 960 | 960 | 4320 | Ford | Fusion | 2011 | N |
| 421940 | 216 | 38 | 03-06-2014 | IN | 100/300 | 1000 | 1384.64 | 5000000 | 600754 | FEMALE | ... | 3 | NO | 66600 | 16650 | 11100 | 38850 | Jeep | Grand Cherokee | 2012 | Y |
| 553565 | 257 | 43 | 18-02-1999 | IN | 500/1000 | 2000 | 929.70 | 6000000 | 618632 | FEMALE | ... | 2 | YES | 63240 | 10540 | 5270 | 47430 | Mercedes | E400 | 2005 | N |
| 331595 | 230 | 39 | 29-11-1999 | IL | 100/300 | 1000 | 904.70 | 7000000 | 454530 | FEMALE | ... | 3 | NO | 74200 | 14840 | 14840 | 44520 | Accura | TL | 2002 | Y |
| 701521 | 270 | 44 | 05-07-2003 | IL | 500/1000 | 2000 | 1030.95 | 0 | 435985 | FEMALE | ... | 0 | NO | 35900 | 7180 | 3590 | 25130 | Audi | A3 | 2007 | Y |
| 958785 | 475 | 57 | 18-02-1995 | OH | 100/300 | 500 | 1216.56 | 0 | 436522 | MALE | ... | 2 | NO | 78000 | 6500 | 13000 | 58500 | Suburu | Forrestor | 2000 | N |
| 563837 | 177 | 33 | 30-12-2002 | IL | 100/300 | 1000 | 1609.67 | 0 | 470128 | MALE | ... | 3 | ? | 82800 | 20700 | 13800 | 48300 | Jeep | Grand Cherokee | 2004 | Y |
| 386690 | 162 | 31 | 21-02-2006 | IN | 100/300 | 1000 | 1050.24 | 0 | 456789 | FEMALE | ... | 0 | NO | 3600 | 360 | 720 | 2520 | BMW | X5 | 2013 | Y |
| 979285 | 154 | 36 | 17-12-2003 | IL | 250/500 | 2000 | 1313.51 | 7000000 | 600904 | FEMALE | ... | 0 | ? | 2800 | 280 | 280 | 2240 | Volkswagen | Passat | 2015 | N |
| 216738 | 10 | 19 | 05-08-2014 | IN | 250/500 | 1000 | 1185.78 | 0 | 478837 | FEMALE | ... | 2 | ? | 48950 | 4450 | 8900 | 35600 | Accura | TL | 2011 | Y |
| 954191 | 273 | 41 | 17-02-2010 | OH | 500/1000 | 1000 | 1403.90 | 0 | 449793 | FEMALE | ... | 2 | YES | 44110 | 4010 | 8020 | 32080 | Honda | Accord | 2015 | N |
| 457244 | 419 | 53 | 28-01-1998 | IL | 500/1000 | 2000 | 736.07 | 6000000 | 445339 | MALE | ... | 0 | YES | 62280 | 5190 | 10380 | 46710 | Suburu | Forrestor | 2012 | N |
| 206667 | 315 | 44 | 05-05-1993 | IL | 250/500 | 1000 | 974.16 | 6000000 | 438328 | FEMALE | ... | 0 | YES | 26730 | 4860 | 4860 | 17010 | Volkswagen | Jetta | 2006 | N |
| 745200 | 72 | 29 | 06-08-1994 | OH | 500/1000 | 500 | 973.80 | 0 | 479913 | FEMALE | ... | 0 | NO | 66200 | 6620 | 6620 | 52960 | Dodge | Neon | 2013 | N |
| 412703 | 32 | 26 | 14-11-2014 | OH | 100/300 | 2000 | 1260.32 | 6000000 | 460760 | MALE | ... | 2 | ? | 45500 | 9100 | 4550 | 31850 | Toyota | Corolla | 2009 | N |
| 736771 | 230 | 41 | 14-12-1991 | IN | 100/300 | 1000 | 1464.03 | 0 | 444797 | MALE | ... | 0 | ? | 53040 | 4420 | 4420 | 44200 | Audi | A3 | 2006 | N |
| 347984 | 157 | 32 | 21-10-2009 | OH | 100/300 | 2000 | 617.11 | 0 | 436711 | MALE | ... | 2 | NO | 50800 | 10160 | 5080 | 35560 | Mercedes | E400 | 2013 | Y |
| 626074 | 265 | 41 | 29-09-1997 | IN | 250/500 | 2000 | 1724.46 | 6000000 | 432491 | FEMALE | ... | 3 | ? | 44200 | 4420 | 4420 | 35360 | Audi | A5 | 2014 | N |
| 999435 | 113 | 29 | 01-01-2008 | OH | 250/500 | 2000 | 1091.73 | 0 | 601213 | MALE | ... | 2 | YES | 49950 | 5550 | 5550 | 38850 | Nissan | Ultima | 2004 | Y |
| 204294 | 7 | 21 | 16-11-1991 | IN | 500/1000 | 1000 | 1342.72 | 0 | 445638 | MALE | ... | 2 | ? | 62460 | 6940 | 6940 | 48580 | Honda | Accord | 2003 | N |
| 751612 | 297 | 48 | 22-06-2009 | IN | 250/500 | 1000 | 1464.73 | 3000000 | 443861 | MALE | ... | 0 | NO | 51480 | 5720 | 5720 | 40040 | Toyota | Highlander | 2013 | N |
| 756981 | 406 | 58 | 02-10-2003 | OH | 250/500 | 2000 | 1117.04 | 0 | 605121 | MALE | ... | 2 | ? | 65520 | 10920 | 5460 | 49140 | Volkswagen | Jetta | 2009 | N |
| 463727 | 6 | 27 | 05-08-1992 | OH | 250/500 | 500 | 1075.71 | 0 | 604328 | FEMALE | ... | 1 | YES | 3190 | 580 | 290 | 2320 | Saab | 95 | 2015 | N |
| 689901 | 344 | 51 | 28-04-1992 | IN | 100/300 | 2000 | 1398.46 | 0 | 455672 | MALE | ... | 2 | NO | 82830 | 7530 | 15060 | 60240 | Audi | A5 | 2004 | N |
| 396224 | 65 | 30 | 08-09-2009 | IN | 100/300 | 500 | 1455.65 | 4000000 | 616706 | FEMALE | ... | 3 | NO | 79800 | 15960 | 7980 | 55860 | Honda | Civic | 1999 | N |
| 682178 | 280 | 41 | 18-12-1994 | OH | 500/1000 | 2000 | 1140.31 | 0 | 473243 | MALE | ... | 3 | YES | 53020 | 9640 | 9640 | 33740 | Toyota | Corolla | 1999 | N |
| 596298 | 269 | 45 | 23-08-1996 | IN | 500/1000 | 500 | 1330.46 | 0 | 435552 | FEMALE | ... | 0 | NO | 24200 | 2200 | 4400 | 17600 | Suburu | Forrestor | 2008 | N |
| 253005 | 275 | 40 | 20-11-1991 | OH | 250/500 | 2000 | 1190.60 | 0 | 434206 | MALE | ... | 3 | YES | 43230 | 7860 | 7860 | 27510 | Chevrolet | Silverado | 2001 | N |
| 631565 | 283 | 43 | 14-07-1997 | IN | 100/300 | 2000 | 1161.91 | 0 | 457722 | FEMALE | ... | 3 | NO | 5850 | 1300 | 650 | 3900 | BMW | M5 | 2006 | N |
| 302669 | 56 | 29 | 29-06-2006 | IL | 100/300 | 1000 | 1523.17 | 0 | 610479 | MALE | ... | 2 | YES | 94560 | 7880 | 15760 | 70920 | Jeep | Grand Cherokee | 1995 | N |
| 971295 | 328 | 49 | 01-10-2001 | OH | 500/1000 | 500 | 1434.51 | 0 | 460535 | FEMALE | ... | 2 | YES | 71440 | 8930 | 8930 | 53580 | Mercedes | ML350 | 2005 | N |
| 525224 | 147 | 37 | 02-10-1992 | IN | 250/500 | 1000 | 1306.78 | 0 | 466818 | MALE | ... | 0 | NO | 81120 | 13520 | 20280 | 47320 | Toyota | Camry | 1995 | N |
| 355085 | 8 | 21 | 09-10-2012 | IN | 500/1000 | 500 | 1021.90 | 0 | 464237 | MALE | ... | 0 | ? | 91260 | 14040 | 14040 | 63180 | Toyota | Corolla | 2012 | N |
| 830729 | 297 | 48 | 10-02-1993 | IN | 100/300 | 1000 | 1538.60 | 0 | 618455 | FEMALE | ... | 0 | ? | 60600 | 6060 | 12120 | 42420 | Ford | Fusion | 2004 | N |
| 651948 | 150 | 31 | 28-09-1994 | IN | 500/1000 | 1000 | 1354.50 | 0 | 456602 | MALE | ... | 3 | YES | 64800 | 6480 | 12960 | 45360 | Suburu | Forrestor | 2000 | Y |
| 424358 | 4 | 34 | 24-05-2003 | OH | 500/1000 | 500 | 1282.93 | 0 | 616126 | FEMALE | ... | 0 | ? | 66880 | 6080 | 12160 | 48640 | Chevrolet | Silverado | 1996 | Y |
| 268833 | 91 | 31 | 18-09-1999 | IN | 100/300 | 1000 | 1338.40 | 4000000 | 431937 | FEMALE | ... | 0 | NO | 60570 | 6730 | 6730 | 47110 | Nissan | Maxima | 2011 | N |
| 287489 | 167 | 36 | 03-02-1994 | IL | 100/300 | 1000 | 949.44 | 0 | 448603 | FEMALE | ... | 0 | NO | 69680 | 8710 | 8710 | 52260 | Mercedes | ML350 | 2008 | Y |
| 497347 | 270 | 45 | 23-08-2003 | OH | 500/1000 | 500 | 1187.53 | 0 | 615383 | FEMALE | ... | 0 | YES | 54340 | 9880 | 0 | 44460 | Saab | 92x | 2005 | N |
| 847123 | 143 | 34 | 19-03-2014 | IL | 100/300 | 500 | 1442.27 | 0 | 435100 | MALE | ... | 3 | YES | 58500 | 11700 | 5850 | 40950 | Dodge | RAM | 1999 | N |
| 172307 | 146 | 32 | 06-12-1993 | OH | 100/300 | 2000 | 1276.43 | 0 | 431278 | MALE | ... | 3 | ? | 59940 | 6660 | 6660 | 46620 | Honda | CRV | 1995 | N |
| 432068 | 61 | 23 | 09-03-2007 | IL | 100/300 | 500 | 1111.72 | 0 | 448857 | MALE | ... | 2 | ? | 41850 | 4650 | 4650 | 32550 | Suburu | Legacy | 1997 | N |
| 903203 | 255 | 44 | 03-01-2004 | OH | 500/1000 | 2000 | 814.96 | 6000000 | 435267 | FEMALE | ... | 2 | NO | 6400 | 640 | 1280 | 4480 | Mercedes | ML350 | 2005 | Y |
| 506333 | 239 | 39 | 22-06-1990 | IL | 100/300 | 500 | 1396.83 | 0 | 442308 | FEMALE | ... | 3 | NO | 76890 | 6990 | 13980 | 55920 | BMW | X6 | 2007 | N |
| 722747 | 20 | 37 | 02-09-2011 | IL | 250/500 | 500 | 1482.14 | 0 | 613936 | FEMALE | ... | 1 | NO | 60750 | 6750 | 6750 | 47250 | Suburu | Forrestor | 2003 | N |
| 248467 | 126 | 30 | 06-10-2012 | IL | 250/500 | 2000 | 1171.75 | 0 | 472163 | FEMALE | ... | 3 | NO | 48730 | 4430 | 4430 | 39870 | Chevrolet | Malibu | 2011 | N |
| 910622 | 78 | 24 | 22-03-1992 | IN | 100/300 | 500 | 1175.51 | 0 | 474792 | MALE | ... | 1 | YES | 7480 | 680 | 680 | 6120 | Dodge | Neon | 2003 | N |
| 137675 | 123 | 28 | 03-12-2012 | IL | 100/300 | 2000 | 1836.02 | 0 | 470559 | MALE | ... | 1 | YES | 79800 | 13300 | 6650 | 59850 | Volkswagen | Passat | 2011 | Y |
| 758740 | 33 | 33 | 04-08-1997 | IL | 500/1000 | 1000 | 1096.79 | 6000000 | 446898 | FEMALE | ... | 1 | ? | 81400 | 8140 | 8140 | 65120 | BMW | M5 | 1998 | N |
| 574637 | 411 | 56 | 30-07-1992 | IL | 250/500 | 1000 | 1595.28 | 0 | 479320 | FEMALE | ... | 0 | ? | 38610 | 3510 | 3510 | 31590 | Volkswagen | Passat | 2007 | N |
| 373600 | 225 | 37 | 01-12-2000 | OH | 100/300 | 1000 | 1217.84 | 5000000 | 443462 | FEMALE | ... | 2 | NO | 57600 | 9600 | 9600 | 38400 | Volkswagen | Passat | 2015 | N |
| 396590 | 460 | 57 | 07-11-1997 | OH | 100/300 | 2000 | 1567.37 | 0 | 602514 | FEMALE | ... | 3 | NO | 58300 | 5830 | 11660 | 40810 | Nissan | Maxima | 2014 | Y |
| 218456 | 245 | 42 | 16-07-2002 | IL | 500/1000 | 1000 | 1575.74 | 7000000 | 614265 | MALE | ... | 2 | ? | 6490 | 590 | 1180 | 4720 | Toyota | Camry | 2011 | Y |
| 971338 | 380 | 56 | 04-11-2004 | OH | 100/300 | 1000 | 1570.86 | 0 | 454685 | MALE | ... | 1 | YES | 58160 | 7270 | 7270 | 43620 | Saab | 95 | 1996 | Y |
| 167231 | 66 | 28 | 26-01-1994 | IN | 100/300 | 2000 | 1472.77 | 0 | 471366 | MALE | ... | 1 | ? | 48290 | 8780 | 8780 | 30730 | Nissan | Maxima | 1995 | N |
| 807369 | 69 | 24 | 19-06-1992 | IN | 500/1000 | 500 | 1418.50 | 0 | 614948 | FEMALE | ... | 0 | NO | 63300 | 12660 | 6330 | 44310 | Dodge | RAM | 2012 | N |
| 203250 | 231 | 43 | 22-04-2010 | IN | 100/300 | 2000 | 1331.69 | 0 | 469653 | FEMALE | ... | 2 | NO | 66950 | 10300 | 10300 | 46350 | Chevrolet | Malibu | 2015 | N |
| 156636 | 261 | 46 | 10-09-2000 | IN | 100/300 | 1000 | 870.55 | 0 | 465631 | MALE | ... | 3 | ? | 80280 | 13380 | 13380 | 53520 | Chevrolet | Tahoe | 2013 | N |
| 284143 | 321 | 44 | 23-04-2008 | IL | 500/1000 | 2000 | 1344.56 | 6000000 | 443344 | MALE | ... | 2 | NO | 4680 | 520 | 0 | 4160 | Accura | MDX | 1999 | N |
| 740518 | 0 | 32 | 18-02-2011 | OH | 500/1000 | 1000 | 1377.04 | 0 | 441363 | MALE | ... | 1 | NO | 39720 | 6620 | 6620 | 26480 | Accura | MDX | 2002 | N |
| 445289 | 405 | 58 | 24-04-2012 | IL | 250/500 | 500 | 1237.88 | 0 | 462683 | MALE | ... | 0 | ? | 63580 | 5780 | 5780 | 52020 | Mercedes | ML350 | 1997 | Y |
| 357949 | 1 | 29 | 24-05-2006 | OH | 500/1000 | 500 | 854.58 | 0 | 612826 | FEMALE | ... | 3 | YES | 86790 | 7890 | 23670 | 55230 | Honda | CRV | 2003 | N |
| 231127 | 289 | 48 | 29-08-1995 | IL | 500/1000 | 500 | 1173.37 | 8000000 | 461744 | FEMALE | ... | 0 | ? | 42900 | 8580 | 0 | 34320 | Accura | TL | 1999 | N |
| 265093 | 83 | 24 | 01-01-2006 | IN | 500/1000 | 1000 | 1070.63 | 0 | 462106 | FEMALE | ... | 1 | NO | 61600 | 6160 | 12320 | 43120 | Honda | CRV | 2003 | N |
| 963680 | 283 | 48 | 04-01-2003 | OH | 500/1000 | 1000 | 1474.66 | 0 | 446755 | FEMALE | ... | 3 | NO | 6560 | 820 | 820 | 4920 | Volkswagen | Jetta | 2003 | N |
| 445694 | 194 | 34 | 24-05-2004 | IL | 250/500 | 1000 | 1193.45 | 0 | 464743 | MALE | ... | 1 | YES | 58800 | 11760 | 5880 | 41160 | Nissan | Pathfinder | 1997 | N |
| 215534 | 184 | 38 | 12-09-1994 | IL | 250/500 | 1000 | 1437.53 | 0 | 437889 | FEMALE | ... | 2 | NO | 53730 | 11940 | 5970 | 35820 | Dodge | RAM | 2013 | Y |
| 168260 | 284 | 48 | 01-03-1991 | OH | 250/500 | 1000 | 1168.80 | 0 | 444232 | FEMALE | ... | 3 | YES | 35750 | 6500 | 3250 | 26000 | Mercedes | E400 | 2001 | N |
| 538955 | 65 | 27 | 29-09-2001 | IN | 100/300 | 1000 | 1164.97 | 0 | 477695 | FEMALE | ... | 2 | YES | 42840 | 3570 | 7140 | 32130 | Chevrolet | Silverado | 2004 | N |
| 243226 | 222 | 39 | 10-01-2012 | IL | 250/500 | 1000 | 1232.72 | 0 | 458237 | MALE | ... | 0 | NO | 87960 | 14660 | 14660 | 58640 | Jeep | Wrangler | 1999 | Y |
| 505014 | 379 | 54 | 27-12-2001 | IL | 100/300 | 500 | 1251.16 | 0 | 447750 | FEMALE | ... | 1 | NO | 75960 | 6330 | 6330 | 63300 | Jeep | Grand Cherokee | 2010 | N |
| 534982 | 354 | 48 | 08-04-2003 | IL | 500/1000 | 2000 | 1526.11 | 5000000 | 469650 | FEMALE | ... | 3 | YES | 90240 | 15040 | 15040 | 60160 | Chevrolet | Malibu | 1995 | N |
| 110408 | 342 | 53 | 14-11-2005 | IN | 100/300 | 1000 | 1028.44 | 0 | 602304 | FEMALE | ... | 0 | NO | 80960 | 14720 | 7360 | 58880 | Accura | MDX | 2000 | N |
| 871432 | 254 | 46 | 15-07-2004 | IL | 250/500 | 2000 | 1472.43 | 0 | 619794 | MALE | ... | 3 | YES | 63580 | 5780 | 11560 | 46240 | Volkswagen | Jetta | 2004 | N |
| 689034 | 138 | 30 | 09-01-2002 | OH | 500/1000 | 500 | 1093.07 | 4000000 | 463291 | FEMALE | ... | 2 | NO | 83160 | 6930 | 13860 | 62370 | Volkswagen | Jetta | 2011 | N |
| 743092 | 239 | 41 | 11-11-2013 | OH | 250/500 | 1000 | 1325.44 | 7000000 | 474898 | FEMALE | ... | 2 | YES | 10790 | 1660 | 830 | 8300 | Mercedes | E400 | 2013 | N |
| 811042 | 131 | 29 | 04-07-2013 | IN | 250/500 | 1000 | 978.27 | 0 | 479821 | FEMALE | ... | 3 | NO | 76400 | 15280 | 7640 | 53480 | Suburu | Forrestor | 2003 | N |
| 505316 | 230 | 43 | 30-06-2002 | IN | 100/300 | 2000 | 1221.14 | 0 | 473394 | MALE | ... | 2 | ? | 75460 | 13720 | 13720 | 48020 | Audi | A5 | 2002 | N |
| 950880 | 210 | 38 | 19-12-1998 | IN | 250/500 | 500 | 999.52 | 0 | 615229 | MALE | ... | 2 | NO | 8640 | 1440 | 720 | 6480 | Accura | TL | 2008 | N |
| 788502 | 101 | 29 | 31-08-2014 | OH | 250/500 | 500 | 1380.89 | 0 | 620197 | MALE | ... | 1 | ? | 67210 | 12220 | 12220 | 42770 | BMW | X6 | 1996 | N |
| 627486 | 153 | 37 | 10-11-2005 | IN | 500/1000 | 500 | 1010.77 | 0 | 438215 | MALE | ... | 3 | NO | 42500 | 4250 | 4250 | 34000 | Volkswagen | Jetta | 1999 | N |
| 618682 | 185 | 35 | 04-03-2000 | IN | 500/1000 | 2000 | 1421.59 | 0 | 442695 | MALE | ... | 3 | YES | 4950 | 900 | 450 | 3600 | Ford | F150 | 2011 | N |
| 663938 | 194 | 38 | 26-01-2011 | IN | 100/300 | 2000 | 1231.25 | 0 | 604333 | FEMALE | ... | 0 | ? | 53680 | 4880 | 9760 | 39040 | Toyota | Camry | 2011 | N |
| 919875 | 27 | 27 | 29-06-2002 | IN | 100/300 | 2000 | 1118.76 | 0 | 470866 | FEMALE | ... | 3 | ? | 61560 | 6840 | 6840 | 47880 | Ford | Fusion | 2008 | N |
| 113464 | 180 | 33 | 19-04-2009 | IN | 500/1000 | 2000 | 1005.47 | 0 | 441871 | FEMALE | ... | 3 | YES | 57700 | 11540 | 5770 | 40390 | Jeep | Grand Cherokee | 2002 | N |
| 403776 | 371 | 54 | 27-04-2012 | IN | 100/300 | 2000 | 1317.97 | 0 | 469853 | MALE | ... | 2 | ? | 32280 | 5380 | 5380 | 21520 | Ford | Fusion | 2010 | Y |
| 502634 | 282 | 46 | 17-08-1991 | OH | 100/300 | 2000 | 1558.86 | 0 | 450800 | MALE | ... | 2 | NO | 69400 | 13880 | 6940 | 48580 | BMW | M5 | 2012 | N |
| 641934 | 51 | 34 | 25-12-2013 | OH | 500/1000 | 500 | 1430.80 | 0 | 461264 | MALE | ... | 3 | NO | 64200 | 6420 | 19260 | 38520 | Honda | Civic | 2007 | Y |
| 633090 | 96 | 27 | 17-02-2009 | IL | 100/300 | 1000 | 1631.10 | 0 | 437323 | FEMALE | ... | 2 | NO | 6030 | 670 | 670 | 4690 | Nissan | Pathfinder | 2007 | N |
| 464234 | 180 | 35 | 17-07-2005 | IL | 500/1000 | 1000 | 1252.48 | 0 | 432148 | MALE | ... | 3 | NO | 5100 | 1020 | 510 | 3570 | Chevrolet | Malibu | 2000 | N |
| 638155 | 463 | 59 | 03-08-1994 | IL | 250/500 | 1000 | 979.73 | 0 | 601848 | FEMALE | ... | 2 | NO | 72400 | 7240 | 14480 | 50680 | Chevrolet | Tahoe | 1999 | Y |
| 106186 | 75 | 25 | 02-12-2011 | IL | 500/1000 | 1000 | 1389.86 | 0 | 472475 | FEMALE | ... | 3 | YES | 65100 | 6510 | 6510 | 52080 | Saab | 93 | 2011 | N |
| 311783 | 193 | 40 | 25-02-2005 | OH | 100/300 | 500 | 1233.85 | 0 | 457463 | FEMALE | ... | 1 | YES | 64260 | 0 | 14280 | 49980 | Ford | Escape | 1999 | N |
| 528385 | 43 | 43 | 07-11-1997 | IL | 500/1000 | 500 | 1320.39 | 0 | 604861 | FEMALE | ... | 1 | ? | 79970 | 7270 | 21810 | 50890 | Honda | CRV | 1996 | Y |
| 710741 | 286 | 41 | 12-09-2001 | IL | 100/300 | 500 | 1106.77 | 0 | 603320 | FEMALE | ... | 0 | NO | 5000 | 500 | 500 | 4000 | Dodge | RAM | 2003 | N |
| 276804 | 3 | 29 | 27-11-1992 | IL | 100/300 | 500 | 995.70 | 5000000 | 615446 | FEMALE | ... | 1 | ? | 5000 | 500 | 1000 | 3500 | Mercedes | E400 | 2008 | Y |
| 507545 | 286 | 41 | 07-12-1998 | IL | 250/500 | 1000 | 1298.85 | 6000000 | 435967 | FEMALE | ... | 3 | YES | 54450 | 6050 | 12100 | 36300 | Saab | 92x | 2007 | N |
| 796375 | 64 | 29 | 22-10-2011 | OH | 250/500 | 2000 | 1202.28 | 0 | 479327 | MALE | ... | 2 | NO | 43700 | 4370 | 4370 | 34960 | Nissan | Pathfinder | 2007 | Y |
| 171183 | 98 | 31 | 01-02-1990 | IN | 100/300 | 500 | 671.92 | 0 | 468300 | MALE | ... | 0 | ? | 64080 | 7120 | 7120 | 49840 | Ford | Escape | 1997 | N |
| 143924 | 75 | 27 | 10-12-1993 | OH | 100/300 | 1000 | 1141.10 | 0 | 468515 | MALE | ... | 1 | YES | 71640 | 5970 | 11940 | 53730 | Toyota | Highlander | 2008 | N |
| 284834 | 110 | 27 | 03-08-2009 | OH | 500/1000 | 1000 | 1664.66 | 0 | 465921 | FEMALE | ... | 3 | ? | 57500 | 5750 | 5750 | 46000 | Audi | A3 | 2010 | N |
| 927205 | 137 | 30 | 16-12-2011 | IL | 250/500 | 500 | 1039.55 | 0 | 466393 | MALE | ... | 1 | ? | 74030 | 6730 | 13460 | 53840 | Chevrolet | Tahoe | 2005 | N |
| 655356 | 56 | 42 | 07-07-1996 | IL | 250/500 | 500 | 1339.39 | 0 | 471786 | FEMALE | ... | 2 | YES | 61490 | 11180 | 11180 | 39130 | BMW | X5 | 1998 | N |
| 432740 | 131 | 33 | 09-10-1990 | IL | 100/300 | 2000 | 1081.17 | 0 | 445120 | MALE | ... | 1 | NO | 4900 | 490 | 490 | 3920 | Toyota | Camry | 2010 | N |
| 893853 | 153 | 34 | 27-02-1994 | IL | 250/500 | 500 | 991.39 | 0 | 449260 | MALE | ... | 0 | NO | 77770 | 14140 | 14140 | 49490 | Nissan | Pathfinder | 2000 | N |
| 594988 | 255 | 43 | 06-05-2007 | IN | 500/1000 | 500 | 984.02 | 0 | 472724 | FEMALE | ... | 2 | ? | 74700 | 7470 | 14940 | 52290 | Nissan | Maxima | 2007 | Y |
| 191891 | 97 | 28 | 11-02-2010 | OH | 100/300 | 1000 | 830.31 | 0 | 443854 | MALE | ... | 0 | ? | 45270 | 10060 | 10060 | 25150 | Jeep | Wrangler | 2006 | N |
| 714346 | 438 | 57 | 05-10-1991 | OH | 500/1000 | 500 | 1119.29 | 0 | 616164 | FEMALE | ... | 0 | NO | 40700 | 4070 | 4070 | 32560 | Volkswagen | Passat | 2000 | Y |
| 326289 | 87 | 27 | 03-01-2004 | OH | 100/300 | 500 | 1048.39 | 0 | 620962 | FEMALE | ... | 1 | YES | 34650 | 6300 | 3150 | 25200 | Ford | F150 | 1996 | N |
| 944537 | 27 | 28 | 23-07-1992 | OH | 500/1000 | 1000 | 1074.47 | 0 | 465201 | MALE | ... | 0 | ? | 3200 | 400 | 400 | 2400 | Jeep | Grand Cherokee | 2012 | N |
| 779156 | 206 | 42 | 10-10-1993 | IL | 500/1000 | 1000 | 1230.76 | 0 | 470488 | MALE | ... | 1 | NO | 78980 | 7180 | 14360 | 57440 | Saab | 92x | 1997 | Y |
| 136520 | 228 | 40 | 01-03-1997 | IN | 100/300 | 500 | 1450.98 | 0 | 478609 | MALE | ... | 2 | NO | 6050 | 1100 | 1100 | 3850 | Toyota | Camry | 2010 | N |
| 912665 | 330 | 47 | 28-05-2014 | IL | 100/300 | 2000 | 1133.27 | 0 | 432218 | FEMALE | ... | 2 | YES | 60500 | 11000 | 5500 | 44000 | Ford | F150 | 1999 | Y |
| 469966 | 128 | 35 | 22-07-2004 | IN | 500/1000 | 500 | 1366.60 | 0 | 620493 | FEMALE | ... | 1 | NO | 88220 | 16040 | 16040 | 56140 | Chevrolet | Malibu | 2008 | N |
| 155912 | 140 | 35 | 21-03-2008 | OH | 100/300 | 1000 | 1520.78 | 0 | 470538 | FEMALE | ... | 2 | YES | 2860 | 520 | 260 | 2080 | Chevrolet | Tahoe | 1997 | Y |
| 409074 | 34 | 34 | 19-03-1992 | OH | 500/1000 | 500 | 1295.87 | 0 | 438529 | FEMALE | ... | 0 | NO | 50800 | 5080 | 5080 | 40640 | Audi | A3 | 1997 | Y |
| 824728 | 290 | 48 | 24-04-2013 | IL | 250/500 | 500 | 1161.03 | 5000000 | 469742 | MALE | ... | 1 | ? | 41520 | 5190 | 5190 | 31140 | Nissan | Ultima | 2014 | N |
| 606037 | 182 | 38 | 10-04-2009 | OH | 500/1000 | 2000 | 1441.06 | 0 | 435534 | FEMALE | ... | 3 | YES | 89650 | 8150 | 16300 | 65200 | Dodge | RAM | 2005 | N |
| 111874 | 143 | 32 | 05-07-2000 | IL | 500/1000 | 1000 | 1464.42 | 0 | 468986 | FEMALE | ... | 0 | NO | 62260 | 5660 | 5660 | 50940 | Saab | 92x | 1995 | N |
| 463513 | 254 | 40 | 23-04-1995 | IL | 250/500 | 500 | 1390.89 | 5000000 | 453719 | MALE | ... | 2 | ? | 53460 | 9720 | 4860 | 38880 | Volkswagen | Jetta | 2009 | N |
| 757352 | 112 | 32 | 21-12-1999 | OH | 500/1000 | 1000 | 1238.92 | 0 | 453400 | MALE | ... | 2 | ? | 5060 | 460 | 920 | 3680 | Honda | CRV | 2012 | N |
| 307469 | 16 | 32 | 28-07-2002 | IL | 100/300 | 1000 | 968.46 | 0 | 615767 | MALE | ... | 2 | ? | 59400 | 6600 | 13200 | 39600 | Dodge | RAM | 1995 | Y |
| 298412 | 107 | 32 | 06-05-2002 | OH | 100/300 | 500 | 1172.82 | 4000000 | 440680 | MALE | ... | 3 | NO | 3900 | 780 | 390 | 2730 | Ford | F150 | 2010 | N |
| 261905 | 252 | 39 | 28-02-2004 | IL | 500/1000 | 500 | 1312.22 | 9000000 | 609949 | MALE | ... | 3 | NO | 59400 | 11880 | 5940 | 41580 | Jeep | Grand Cherokee | 2010 | Y |
| 674485 | 303 | 43 | 14-01-1999 | OH | 500/1000 | 1000 | 671.01 | 7000000 | 479655 | FEMALE | ... | 0 | ? | 60210 | 6690 | 6690 | 46830 | Nissan | Maxima | 2013 | N |
| 223404 | 101 | 32 | 23-01-2002 | IL | 250/500 | 500 | 895.14 | 0 | 439964 | MALE | ... | 3 | YES | 43600 | 8720 | 4360 | 30520 | Suburu | Legacy | 2010 | N |
| 941807 | 316 | 46 | 25-06-2011 | OH | 100/300 | 500 | 1219.94 | 7000000 | 431968 | FEMALE | ... | 1 | YES | 63100 | 6310 | 12620 | 44170 | Accura | TL | 2000 | N |
| 437442 | 344 | 51 | 27-06-2008 | IL | 100/300 | 1000 | 959.83 | 0 | 435809 | FEMALE | ... | 2 | YES | 75400 | 17400 | 11600 | 46400 | BMW | X6 | 2006 | Y |
| 730973 | 209 | 36 | 11-01-2010 | IN | 100/300 | 2000 | 1223.39 | 0 | 452218 | FEMALE | ... | 3 | ? | 65440 | 8180 | 8180 | 49080 | Jeep | Wrangler | 2014 | N |
| 998865 | 80 | 28 | 05-12-2014 | IL | 500/1000 | 1000 | 1740.57 | 0 | 442142 | FEMALE | ... | 1 | ? | 33480 | 3720 | 3720 | 26040 | Dodge | Neon | 2011 | N |
| 749325 | 276 | 45 | 22-03-2000 | IL | 500/1000 | 500 | 948.10 | 0 | 430621 | FEMALE | ... | 2 | ? | 69300 | 13860 | 6930 | 48510 | Ford | Escape | 2010 | N |
| 698470 | 222 | 38 | 17-06-2008 | IN | 100/300 | 2000 | 1157.97 | 0 | 433853 | MALE | ... | 2 | YES | 60480 | 6720 | 6720 | 47040 | Accura | TL | 2001 | N |
| 238196 | 194 | 41 | 15-02-1993 | IL | 250/500 | 500 | 1203.81 | 0 | 613119 | MALE | ... | 2 | ? | 95900 | 13700 | 20550 | 61650 | Saab | 95 | 1999 | N |
| 206004 | 199 | 38 | 26-09-1991 | IL | 250/500 | 1000 | 1281.25 | 0 | 467780 | FEMALE | ... | 2 | NO | 44440 | 8080 | 4040 | 32320 | BMW | X6 | 2007 | Y |
| 654974 | 73 | 30 | 10-05-2009 | OH | 100/300 | 500 | 803.36 | 0 | 435371 | FEMALE | ... | 0 | YES | 57000 | 0 | 11400 | 45600 | Audi | A3 | 2013 | N |
| 641845 | 176 | 36 | 30-03-1995 | OH | 250/500 | 500 | 1865.83 | 5000000 | 436173 | MALE | ... | 1 | YES | 47300 | 4300 | 8600 | 34400 | Volkswagen | Jetta | 2006 | N |
| 794534 | 145 | 37 | 14-12-1991 | OH | 250/500 | 2000 | 1434.27 | 0 | 457234 | FEMALE | ... | 3 | ? | 55110 | 5010 | 10020 | 40080 | Nissan | Maxima | 2002 | N |
| 639027 | 270 | 41 | 21-06-1994 | IL | 250/500 | 1000 | 817.28 | 0 | 460263 | MALE | ... | 1 | NO | 60190 | 4630 | 9260 | 46300 | Mercedes | ML350 | 2014 | Y |
| 669800 | 456 | 62 | 24-06-2009 | OH | 250/500 | 1000 | 1395.77 | 0 | 611651 | FEMALE | ... | 3 | NO | 66480 | 5540 | 11080 | 49860 | Saab | 92x | 2012 | Y |
| 153298 | 130 | 34 | 23-03-2009 | OH | 100/300 | 500 | 990.11 | 0 | 442666 | MALE | ... | 3 | YES | 5830 | 1060 | 1060 | 3710 | Dodge | RAM | 2015 | N |
| 804751 | 189 | 39 | 11-09-1997 | OH | 250/500 | 2000 | 838.02 | 0 | 450702 | FEMALE | ... | 0 | YES | 64400 | 6440 | 6440 | 51520 | Dodge | Neon | 1997 | N |
| 713172 | 140 | 32 | 23-10-1996 | IL | 250/500 | 1000 | 985.97 | 5000000 | 457793 | FEMALE | ... | 3 | ? | 82170 | 14940 | 7470 | 59760 | Chevrolet | Silverado | 1995 | Y |
| 621756 | 243 | 41 | 21-04-1997 | IN | 100/300 | 1000 | 1129.23 | 0 | 470190 | FEMALE | ... | 0 | YES | 50300 | 10060 | 5030 | 35210 | Suburu | Legacy | 1999 | Y |
| 947598 | 219 | 43 | 20-06-2002 | IN | 100/300 | 1000 | 1114.29 | 0 | 465136 | FEMALE | ... | 2 | YES | 66660 | 6060 | 6060 | 54540 | Toyota | Highlander | 2006 | N |
| 374545 | 149 | 34 | 28-08-2005 | IN | 250/500 | 500 | 664.86 | 0 | 608963 | FEMALE | ... | 1 | NO | 105040 | 16160 | 16160 | 72720 | Dodge | RAM | 1999 | N |
| 180286 | 160 | 33 | 08-02-2009 | IL | 500/1000 | 1000 | 1422.78 | 0 | 616583 | FEMALE | ... | 3 | YES | 52800 | 5280 | 5280 | 42240 | Nissan | Pathfinder | 2006 | N |
| 662088 | 224 | 42 | 06-03-2005 | OH | 500/1000 | 500 | 1212.75 | 0 | 455913 | FEMALE | ... | 0 | YES | 55200 | 9200 | 13800 | 32200 | Honda | Civic | 1998 | N |
| 178081 | 385 | 51 | 20-07-1990 | IN | 250/500 | 1000 | 976.37 | 0 | 602842 | FEMALE | ... | 3 | ? | 67600 | 13520 | 6760 | 47320 | Suburu | Legacy | 2007 | N |
| 398683 | 373 | 55 | 30-04-2007 | IN | 250/500 | 500 | 1607.36 | 0 | 444626 | MALE | ... | 2 | NO | 60600 | 6060 | 12120 | 42420 | Dodge | RAM | 2007 | Y |
| 786432 | 435 | 58 | 15-11-1997 | IN | 100/300 | 2000 | 1145.85 | 0 | 471784 | MALE | ... | 1 | YES | 41490 | 9220 | 4610 | 27660 | Mercedes | E400 | 2004 | N |
| 938634 | 144 | 35 | 30-08-1993 | IL | 100/300 | 500 | 1427.46 | 0 | 444922 | MALE | ... | 0 | ? | 87900 | 17580 | 8790 | 61530 | Dodge | Neon | 1995 | N |
| 482495 | 92 | 32 | 29-01-1998 | IL | 500/1000 | 500 | 1592.41 | 0 | 474324 | MALE | ... | 3 | YES | 53400 | 5340 | 5340 | 42720 | Jeep | Wrangler | 1996 | N |
| 327488 | 155 | 35 | 09-08-1993 | OH | 250/500 | 1000 | 919.37 | 0 | 459537 | FEMALE | ... | 3 | ? | 48360 | 8060 | 8060 | 32240 | Nissan | Maxima | 1997 | N |
| 715202 | 78 | 31 | 02-04-1991 | OH | 250/500 | 1000 | 1377.23 | 0 | 440757 | FEMALE | ... | 1 | ? | 52290 | 5810 | 11620 | 34860 | Nissan | Maxima | 1997 | N |
| 516959 | 264 | 43 | 01-05-2010 | IL | 100/300 | 500 | 1508.12 | 6000000 | 433275 | MALE | ... | 1 | NO | 60750 | 13500 | 6750 | 40500 | Jeep | Wrangler | 2015 | Y |
| 984456 | 66 | 30 | 24-06-2003 | IN | 500/1000 | 500 | 484.67 | 0 | 608309 | FEMALE | ... | 2 | YES | 65560 | 11920 | 11920 | 41720 | Volkswagen | Passat | 2015 | Y |
| 786103 | 188 | 37 | 24-09-1994 | OH | 100/300 | 500 | 1457.21 | 0 | 471785 | FEMALE | ... | 0 | YES | 45000 | 5000 | 5000 | 35000 | Suburu | Forrestor | 2003 | N |
| 185124 | 169 | 37 | 07-12-2001 | IL | 100/300 | 1000 | 936.19 | 0 | 454776 | MALE | ... | 1 | YES | 57900 | 17370 | 5790 | 34740 | Audi | A5 | 2005 | N |
| 362407 | 209 | 39 | 06-12-1996 | IN | 100/300 | 500 | 1264.99 | 0 | 614169 | MALE | ... | 1 | NO | 37800 | 8400 | 4200 | 25200 | Chevrolet | Silverado | 1995 | N |
| 389525 | 210 | 37 | 10-07-2012 | OH | 500/1000 | 500 | 1467.76 | 0 | 601425 | FEMALE | ... | 3 | YES | 63300 | 6330 | 6330 | 50640 | Toyota | Highlander | 2000 | N |
| 488724 | 239 | 40 | 29-11-2004 | IN | 100/300 | 500 | 1463.95 | 0 | 430567 | FEMALE | ... | 0 | YES | 69740 | 6340 | 6340 | 57060 | Dodge | Neon | 2003 | N |
| 338070 | 446 | 61 | 25-01-2006 | IN | 500/1000 | 1000 | 1037.32 | 0 | 438837 | FEMALE | ... | 1 | NO | 80880 | 6740 | 13480 | 60660 | Nissan | Ultima | 2005 | N |
| 865607 | 476 | 61 | 18-04-1993 | IN | 250/500 | 1000 | 1562.80 | 0 | 458997 | FEMALE | ... | 2 | YES | 49390 | 8980 | 4490 | 35920 | Suburu | Legacy | 2009 | N |
| 725330 | 169 | 34 | 21-07-1996 | IN | 100/300 | 500 | 1469.75 | 0 | 458132 | FEMALE | ... | 0 | YES | 38700 | 7740 | 3870 | 27090 | Volkswagen | Passat | 2012 | N |
| 272330 | 297 | 47 | 29-11-2009 | IN | 250/500 | 500 | 1616.65 | 7000000 | 456363 | MALE | ... | 3 | YES | 44400 | 5550 | 5550 | 33300 | Jeep | Grand Cherokee | 1999 | N |
| 728025 | 421 | 56 | 15-02-1990 | IN | 100/300 | 500 | 1935.85 | 4000000 | 470826 | MALE | ... | 3 | ? | 92730 | 16860 | 8430 | 67440 | Mercedes | E400 | 2004 | Y |
| 753056 | 140 | 36 | 03-05-1991 | IN | 250/500 | 500 | 1363.59 | 0 | 468303 | FEMALE | ... | 0 | YES | 79500 | 7950 | 7950 | 63600 | Chevrolet | Tahoe | 2000 | N |
| 910365 | 200 | 34 | 19-12-2001 | IN | 250/500 | 1000 | 828.42 | 3000000 | 467762 | FEMALE | ... | 2 | NO | 56000 | 5600 | 5600 | 44800 | Chevrolet | Malibu | 2009 | N |
| 824116 | 135 | 34 | 05-05-1998 | IL | 250/500 | 2000 | 1687.53 | 0 | 465674 | FEMALE | ... | 2 | NO | 78200 | 15640 | 7820 | 54740 | Audi | A3 | 2009 | N |
| 322613 | 110 | 33 | 16-04-1995 | IN | 250/500 | 1000 | 1183.48 | 0 | 442389 | MALE | ... | 3 | NO | 55200 | 13800 | 9200 | 32200 | Saab | 93 | 2015 | Y |
| 886473 | 282 | 48 | 10-03-1991 | OH | 500/1000 | 2000 | 1422.56 | 7000000 | 473705 | FEMALE | ... | 2 | NO | 3520 | 640 | 320 | 2560 | Accura | MDX | 2013 | N |
| 833321 | 215 | 38 | 01-03-2010 | IN | 250/500 | 500 | 1405.71 | 0 | 465376 | FEMALE | ... | 1 | NO | 70700 | 7070 | 14140 | 49490 | Volkswagen | Passat | 2008 | N |
| 521592 | 140 | 30 | 15-06-2014 | IL | 100/300 | 500 | 1354.20 | 0 | 438775 | FEMALE | ... | 0 | ? | 60170 | 5470 | 10940 | 43760 | Nissan | Pathfinder | 2006 | N |
| 254837 | 250 | 42 | 25-11-2004 | IN | 100/300 | 500 | 1055.60 | 0 | 457962 | MALE | ... | 1 | ? | 74800 | 13600 | 6800 | 54400 | Ford | Fusion | 2009 | Y |
| 476839 | 65 | 29 | 09-08-1990 | IL | 250/500 | 1000 | 1726.91 | 0 | 456570 | MALE | ... | 0 | ? | 7200 | 720 | 1440 | 5040 | Audi | A5 | 1999 | N |
| 149601 | 187 | 34 | 28-03-2003 | IN | 500/1000 | 500 | 1232.57 | 0 | 612986 | FEMALE | ... | 0 | ? | 45100 | 8200 | 4100 | 32800 | Nissan | Pathfinder | 2011 | N |
| 630683 | 386 | 53 | 23-10-2007 | OH | 250/500 | 500 | 1078.65 | 0 | 615730 | MALE | ... | 3 | YES | 66660 | 12120 | 6060 | 48480 | Honda | Civic | 2006 | N |
| 569245 | 293 | 49 | 05-12-1995 | IL | 100/300 | 2000 | 1239.06 | 0 | 439360 | FEMALE | ... | 1 | ? | 57310 | 5210 | 10420 | 41680 | Volkswagen | Passat | 2002 | N |
| 907012 | 179 | 32 | 15-12-1996 | OH | 500/1000 | 2000 | 1246.68 | 0 | 440251 | FEMALE | ... | 1 | ? | 53100 | 5900 | 5900 | 41300 | Suburu | Impreza | 2006 | N |
| 528259 | 370 | 55 | 22-12-2012 | IN | 500/1000 | 2000 | 1389.13 | 7000000 | 456203 | MALE | ... | 2 | ? | 9000 | 900 | 1800 | 6300 | Mercedes | ML350 | 2015 | N |
| 728839 | 233 | 39 | 02-01-2001 | OH | 500/1000 | 2000 | 1524.18 | 0 | 605220 | MALE | ... | 0 | YES | 48870 | 5430 | 5430 | 38010 | Saab | 95 | 1999 | N |
| 264221 | 297 | 48 | 28-07-2014 | IL | 500/1000 | 1000 | 1243.68 | 0 | 463331 | MALE | ... | 2 | ? | 54960 | 6870 | 0 | 48090 | Toyota | Corolla | 2002 | Y |
| 672416 | 147 | 37 | 20-04-2013 | IN | 500/1000 | 2000 | 1375.29 | 0 | 609226 | FEMALE | ... | 1 | ? | 56160 | 6240 | 6240 | 43680 | Ford | Fusion | 2015 | Y |
| 521854 | 198 | 36 | 16-02-2001 | IN | 250/500 | 1000 | 1096.39 | 0 | 603848 | MALE | ... | 3 | YES | 49410 | 5490 | 5490 | 38430 | Audi | A3 | 2015 | N |
| 737252 | 290 | 45 | 18-11-1993 | OH | 500/1000 | 2000 | 1215.36 | 0 | 617739 | MALE | ... | 1 | NO | 66200 | 6620 | 6620 | 52960 | Suburu | Impreza | 2012 | N |
| 898519 | 233 | 43 | 21-05-2000 | OH | 250/500 | 1000 | 954.18 | 0 | 437470 | FEMALE | ... | 3 | YES | 42500 | 8500 | 4250 | 29750 | Nissan | Pathfinder | 2000 | N |
| 101421 | 91 | 26 | 19-10-1999 | IL | 250/500 | 1000 | 1022.46 | 0 | 444896 | FEMALE | ... | 2 | ? | 74200 | 7420 | 7420 | 59360 | Jeep | Wrangler | 1996 | N |
| 737483 | 218 | 43 | 14-02-1996 | IL | 250/500 | 500 | 1521.55 | 0 | 477856 | FEMALE | ... | 3 | YES | 68200 | 13640 | 6820 | 47740 | Dodge | RAM | 2003 | Y |
| 979963 | 260 | 43 | 03-06-2009 | IN | 100/300 | 500 | 982.22 | 0 | 604279 | FEMALE | ... | 0 | ? | 75400 | 15080 | 7540 | 52780 | Honda | CRV | 2011 | N |
| 130156 | 322 | 49 | 24-09-2001 | IL | 250/500 | 2000 | 1277.12 | 0 | 431853 | FEMALE | ... | 2 | YES | 42240 | 7680 | 7680 | 26880 | Chevrolet | Malibu | 2007 | N |
| 676255 | 405 | 57 | 28-12-1999 | IN | 500/1000 | 1000 | 1132.47 | 4000000 | 434293 | MALE | ... | 3 | ? | 61440 | 10240 | 10240 | 40960 | Saab | 93 | 2008 | N |
| 815883 | 398 | 55 | 02-07-1991 | OH | 250/500 | 2000 | 1305.26 | 0 | 455482 | MALE | ... | 3 | YES | 36740 | 3340 | 6680 | 26720 | BMW | X5 | 1998 | Y |
| 258265 | 9 | 30 | 10-04-1994 | IL | 100/300 | 1000 | 1073.83 | 0 | 438877 | FEMALE | ... | 0 | NO | 85690 | 15580 | 15580 | 54530 | BMW | 3 Series | 2011 | N |
| 698589 | 479 | 60 | 28-11-2002 | IL | 500/1000 | 1000 | 1188.45 | 0 | 459295 | FEMALE | ... | 3 | ? | 53900 | 5390 | 10780 | 37730 | Saab | 95 | 2006 | N |
| 330119 | 215 | 35 | 15-06-2004 | IL | 500/1000 | 1000 | 1125.40 | 0 | 606144 | MALE | ... | 1 | NO | 2640 | 220 | 440 | 1980 | Jeep | Wrangler | 2001 | N |
| 927354 | 45 | 31 | 15-09-1990 | IN | 100/300 | 500 | 1459.50 | 0 | 475891 | MALE | ... | 3 | ? | 6000 | 1000 | 1000 | 4000 | Suburu | Impreza | 2000 | N |
| 231508 | 156 | 38 | 16-09-2009 | IL | 100/300 | 500 | 1367.99 | 0 | 462525 | MALE | ... | 3 | ? | 55200 | 11040 | 5520 | 38640 | Saab | 92x | 1998 | Y |
| 868031 | 425 | 53 | 24-06-1990 | OH | 250/500 | 2000 | 912.29 | 0 | 464808 | FEMALE | ... | 2 | ? | 97080 | 16180 | 16180 | 64720 | Saab | 92x | 2005 | N |
| 744557 | 192 | 38 | 25-02-2011 | IN | 500/1000 | 1000 | 1281.43 | 0 | 432405 | FEMALE | ... | 2 | NO | 30700 | 3070 | 6140 | 21490 | Jeep | Wrangler | 2010 | N |
| 727109 | 98 | 26 | 20-02-2001 | IN | 500/1000 | 2000 | 1082.10 | 0 | 477268 | MALE | ... | 1 | YES | 65430 | 14540 | 7270 | 43620 | Jeep | Wrangler | 2001 | N |
| 608443 | 259 | 45 | 21-12-2006 | IL | 500/1000 | 2000 | 1175.07 | 0 | 457121 | MALE | ... | 1 | NO | 87780 | 7980 | 7980 | 71820 | Honda | CRV | 2011 | N |
| 727443 | 186 | 38 | 01-07-2013 | OH | 100/300 | 500 | 922.85 | 0 | 471148 | MALE | ... | 1 | YES | 3600 | 400 | 400 | 2800 | Honda | Civic | 1999 | N |
| 398484 | 277 | 46 | 07-11-1992 | IL | 250/500 | 2000 | 1177.57 | 0 | 469220 | FEMALE | ... | 3 | NO | 3870 | 430 | 860 | 2580 | Jeep | Wrangler | 2010 | N |
| 752504 | 211 | 38 | 15-05-1997 | IN | 250/500 | 1000 | 1055.09 | 0 | 433250 | FEMALE | ... | 3 | YES | 91520 | 8320 | 16640 | 66560 | BMW | X6 | 2005 | Y |
| 967713 | 243 | 44 | 25-12-1997 | IL | 250/500 | 500 | 809.11 | 0 | 600208 | MALE | ... | 1 | YES | 51400 | 5140 | 10280 | 35980 | Honda | Civic | 1996 | N |
| 840225 | 154 | 34 | 05-10-1999 | OH | 100/300 | 1000 | 1041.36 | 0 | 448436 | FEMALE | ... | 3 | YES | 74360 | 13520 | 13520 | 47320 | Toyota | Highlander | 2005 | Y |
| 643226 | 204 | 40 | 07-04-1992 | OH | 250/500 | 1000 | 1693.83 | 7000000 | 447976 | MALE | ... | 0 | ? | 53400 | 5340 | 5340 | 42720 | Honda | CRV | 2003 | N |
| 535879 | 458 | 59 | 05-03-2009 | IN | 100/300 | 1000 | 1685.69 | 0 | 472236 | FEMALE | ... | 2 | YES | 71800 | 14360 | 14360 | 43080 | Jeep | Grand Cherokee | 1995 | N |
| 746630 | 147 | 31 | 10-02-1997 | IN | 250/500 | 500 | 1054.92 | 6000000 | 468232 | FEMALE | ... | 0 | ? | 68240 | 8530 | 0 | 59710 | Toyota | Corolla | 2013 | Y |
| 598308 | 279 | 45 | 28-01-1992 | IN | 250/500 | 2000 | 1333.97 | 6000000 | 620819 | FEMALE | ... | 0 | ? | 61050 | 11100 | 11100 | 38850 | Dodge | RAM | 2011 | Y |
| 720356 | 308 | 47 | 16-09-2013 | OH | 100/300 | 1000 | 1013.61 | 6000000 | 452349 | FEMALE | ... | 1 | YES | 5590 | 860 | 860 | 3870 | Suburu | Impreza | 2002 | N |
| 724752 | 284 | 48 | 16-05-2008 | IL | 500/1000 | 500 | 958.30 | 0 | 464646 | FEMALE | ... | 0 | ? | 46860 | 8520 | 8520 | 29820 | Volkswagen | Passat | 1998 | N |
| 231548 | 31 | 32 | 07-09-1999 | IL | 100/300 | 2000 | 1263.48 | 4000000 | 442948 | FEMALE | ... | 0 | ? | 63600 | 5300 | 10600 | 47700 | Audi | A5 | 1997 | Y |
| 193213 | 407 | 55 | 11-03-1996 | OH | 100/300 | 1000 | 1250.08 | 5000000 | 474598 | FEMALE | ... | 3 | YES | 68160 | 11360 | 11360 | 45440 | Ford | Escape | 2010 | N |
| 125591 | 187 | 37 | 08-08-2013 | IN | 500/1000 | 1000 | 1412.06 | 5000000 | 450947 | FEMALE | ... | 3 | ? | 57700 | 5770 | 5770 | 46160 | Nissan | Maxima | 2000 | N |
| 437960 | 128 | 35 | 03-04-2001 | IN | 250/500 | 1000 | 1074.99 | 0 | 453620 | FEMALE | ... | 0 | ? | 7590 | 1380 | 690 | 5520 | Accura | MDX | 2012 | N |
| 390256 | 163 | 37 | 25-11-2009 | IN | 500/1000 | 1000 | 1200.33 | 4000000 | 477631 | FEMALE | ... | 1 | YES | 3900 | 390 | 780 | 2730 | Volkswagen | Jetta | 2008 | Y |
| 488597 | 80 | 26 | 08-05-2001 | IL | 100/300 | 1000 | 1343.00 | 0 | 443625 | MALE | ... | 0 | NO | 90400 | 9040 | 9040 | 72320 | BMW | 3 Series | 1995 | N |
| 844062 | 398 | 55 | 25-05-1990 | OH | 250/500 | 500 | 862.19 | 0 | 606858 | MALE | ... | 3 | ? | 6600 | 600 | 1200 | 4800 | Accura | MDX | 2012 | N |
| 221854 | 232 | 44 | 03-10-1994 | OH | 250/500 | 2000 | 1181.64 | 0 | 454552 | MALE | ... | 1 | YES | 55400 | 5540 | 11080 | 38780 | Jeep | Grand Cherokee | 2002 | Y |
| 889764 | 143 | 33 | 30-11-1993 | OH | 500/1000 | 1000 | 1200.09 | 0 | 454191 | FEMALE | ... | 2 | ? | 70400 | 14080 | 7040 | 49280 | Accura | RSX | 2002 | N |
| 929306 | 266 | 42 | 06-03-2003 | IN | 100/300 | 500 | 1093.83 | 4000000 | 468454 | MALE | ... | 1 | NO | 53280 | 4440 | 8880 | 39960 | Suburu | Impreza | 2015 | Y |
| 515457 | 89 | 32 | 18-12-1996 | IN | 250/500 | 1000 | 988.93 | 0 | 614187 | FEMALE | ... | 3 | YES | 84590 | 15380 | 15380 | 53830 | Dodge | Neon | 1999 | N |
| 525862 | 50 | 44 | 18-10-2000 | OH | 250/500 | 2000 | 1188.51 | 0 | 447469 | MALE | ... | 2 | NO | 61100 | 6110 | 12220 | 42770 | Dodge | Neon | 2008 | N |
| 490514 | 230 | 43 | 09-02-2007 | IN | 500/1000 | 2000 | 1101.83 | 0 | 451529 | MALE | ... | 3 | YES | 51900 | 5190 | 10380 | 36330 | BMW | M5 | 2011 | Y |
| 774895 | 17 | 39 | 28-10-2006 | IL | 250/500 | 1000 | 840.95 | 0 | 431202 | FEMALE | ... | 1 | ? | 3440 | 430 | 430 | 2580 | Suburu | Legacy | 2002 | N |
| 669809 | 29 | 32 | 05-04-2002 | OH | 100/300 | 1000 | 1722.50 | 0 | 453713 | MALE | ... | 2 | ? | 76900 | 7690 | 7690 | 61520 | Jeep | Wrangler | 1995 | N |
| 836349 | 235 | 39 | 01-05-2013 | IL | 500/1000 | 2000 | 1453.61 | 4000000 | 619570 | MALE | ... | 3 | ? | 60320 | 9280 | 9280 | 41760 | Chevrolet | Tahoe | 2012 | Y |
| 663190 | 286 | 43 | 05-02-1994 | IL | 100/300 | 500 | 1564.43 | 3000000 | 477644 | FEMALE | ... | 2 | YES | 34290 | 3810 | 3810 | 26670 | Jeep | Grand Cherokee | 2013 | N |
| 674570 | 124 | 28 | 08-12-2001 | OH | 250/500 | 1000 | 1235.14 | 0 | 443567 | MALE | ... | 1 | ? | 60200 | 6020 | 6020 | 48160 | Volkswagen | Passat | 2012 | N |
| 681486 | 141 | 30 | 24-03-2007 | IN | 500/1000 | 1000 | 1347.04 | 0 | 430665 | MALE | ... | 2 | YES | 6480 | 540 | 1080 | 4860 | Honda | Civic | 1996 | N |
| 918516 | 130 | 34 | 17-02-2003 | OH | 250/500 | 500 | 1383.49 | 3000000 | 442797 | FEMALE | ... | 3 | YES | 67500 | 7500 | 7500 | 52500 | Suburu | Impreza | 1996 | N |
| 533940 | 458 | 62 | 18-11-2011 | IL | 500/1000 | 2000 | 1356.92 | 5000000 | 441714 | MALE | ... | 1 | YES | 46980 | 5220 | 5220 | 36540 | Audi | A5 | 1998 | N |
| 556080 | 456 | 60 | 11-11-1996 | OH | 250/500 | 1000 | 766.19 | 0 | 612260 | FEMALE | ... | 3 | ? | 5060 | 460 | 920 | 3680 | Mercedes | E400 | 2007 | N |
360 rows × 38 columns
fraud_df.replace({'property_damage':'?'},{'property_damage':'Unknown'},inplace=True)
fraud_df.replace({'collision_type':'?'},{'collision_type':'Unknown'},inplace=True)
fraud_df.replace({'police_report_available':'?'},{'police_report_available':'Unknown'},inplace=True)
fraud_df[cat].describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| policy_bind_date | 1000 | 951 | 01-01-2006 | 3 |
| policy_state | 1000 | 3 | OH | 352 |
| policy_csl | 1000 | 3 | 250/500 | 351 |
| insured_sex | 1000 | 2 | FEMALE | 537 |
| insured_education_level | 1000 | 7 | JD | 161 |
| insured_occupation | 1000 | 14 | machine-op-inspct | 93 |
| insured_hobbies | 1000 | 20 | reading | 64 |
| insured_relationship | 1000 | 6 | own-child | 183 |
| incident_date | 1000 | 60 | 02-02-2015 | 28 |
| incident_type | 1000 | 4 | Multi-vehicle Collision | 419 |
| collision_type | 1000 | 4 | Rear Collision | 292 |
| incident_severity | 1000 | 4 | Minor Damage | 354 |
| authorities_contacted | 1000 | 5 | Police | 292 |
| incident_state | 1000 | 7 | NY | 262 |
| incident_city | 1000 | 7 | Springfield | 157 |
| incident_location | 1000 | 1000 | 9043 Maple Hwy | 1 |
| property_damage | 1000 | 3 | Unknown | 360 |
| police_report_available | 1000 | 3 | NO | 343 |
| auto_make | 1000 | 14 | Suburu | 80 |
| auto_model | 1000 | 39 | RAM | 43 |
| fraud_reported | 1000 | 2 | N | 753 |
plt.figure(figsize=(10,10))
fraud_df.boxplot(vert=0)
<AxesSubplot:>
# fraud_df_cp.drop('policy_bind_date',axis=1,inplace=True)
# fraud_df_cp.drop('incident_date',axis=1,inplace=True)
#fraud_df_X = fraud_df_cp.drop('fraud_reported',axis=1)
#fraud_df_Y = fraud_df_cp_upd['fraud_reported']
fraud_df_X = fraud_df.drop('fraud_reported',axis=1)
fraud_df_Y = fraud_df['fraud_reported']
fraud_df_X.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 1000 non-null int64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 1000 non-null float64 7 umbrella_limit 1000 non-null int64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null object 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 1000 non-null int64 31 injury_claim 1000 non-null int64 32 property_claim 1000 non-null int64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 dtypes: float64(1), int64(16), object(20) memory usage: 336.9+ KB
Q1 = fraud_df_X.quantile(0.25)
Q3=fraud_df_X.quantile(0.75)
IQR=Q3-Q1
UL = Q3+1.5*IQR
LL=Q1-1.5*IQR
((fraud_df_X > UL) | (fraud_df_X < LL)).sum()
age 4 authorities_contacted 0 auto_make 0 auto_model 0 auto_year 0 bodily_injuries 0 capital-gains 0 capital-loss 0 collision_type 0 incident_city 0 incident_date 0 incident_hour_of_the_day 0 incident_location 0 incident_severity 0 incident_state 0 incident_type 0 injury_claim 0 insured_education_level 0 insured_hobbies 0 insured_occupation 0 insured_relationship 0 insured_sex 0 insured_zip 0 months_as_customer 0 number_of_vehicles_involved 0 police_report_available 0 policy_annual_premium 9 policy_bind_date 0 policy_csl 0 policy_deductable 0 policy_state 0 property_claim 6 property_damage 0 total_claim_amount 1 umbrella_limit 201 vehicle_claim 0 witnesses 0 dtype: int64
fraud_df_X[((fraud_df_X > UL) | (fraud_df_X < LL))]=np.nan
fraud_df_X.isnull().sum()
months_as_customer 0 age 4 policy_bind_date 0 policy_state 0 policy_csl 0 policy_deductable 0 policy_annual_premium 9 umbrella_limit 201 insured_zip 0 insured_sex 0 insured_education_level 0 insured_occupation 0 insured_hobbies 0 insured_relationship 0 capital-gains 0 capital-loss 0 incident_date 0 incident_type 0 collision_type 0 incident_severity 0 authorities_contacted 0 incident_state 0 incident_city 0 incident_location 0 incident_hour_of_the_day 0 number_of_vehicles_involved 0 property_damage 0 bodily_injuries 0 witnesses 0 police_report_available 0 total_claim_amount 1 injury_claim 0 property_claim 6 vehicle_claim 0 auto_make 0 auto_model 0 auto_year 0 dtype: int64
fraud_df_X.isnull().sum().sum()
221
fraud_df_up = pd.concat([fraud_df_X,fraud_df_Y],axis=1)
plt.figure(figsize=(12,8))
sns.heatmap(fraud_df_up.isnull(),cbar=False,cmap='coolwarm',yticklabels=False)
plt.show()
fraud_df_up.isnull().sum(axis=1)
policy_number 521585 0 342868 1 687698 1 227811 1 367455 1 104594 0 413978 0 429027 0 485665 0 636550 0 543610 1 214618 1 842643 1 626808 0 644081 0 892874 0 558938 1 275265 1 921202 0 143972 0 183430 1 431876 0 285496 0 115399 0 736882 0 699044 0 863236 0 608513 1 914088 0 596785 0 908616 0 666333 1 336614 0 584859 0 990493 0 129872 1 200152 0 933293 0 485664 0 982871 0 206213 0 616337 0 448961 0 790442 1 108844 0 430029 0 529112 0 939631 1 866931 1 582011 0 691189 1 537546 0 394975 0 729634 0 282195 0 420810 0 524836 0 307195 0 623648 0 485372 0 598554 0 303987 0 343161 0 519312 0 132902 1 332867 0 356590 1 346002 1 500533 0 348209 0 486676 0 260845 0 657045 0 761189 0 175177 0 116700 0 166264 0 527945 0 627540 1 279422 0 484200 0 645258 1 694662 1 960680 0 498140 0 498875 0 798177 1 614763 0 679370 1 958857 0 686816 1 127754 1 918629 0 731450 0 307447 0 992145 1 900628 0 235220 0 740019 0 246882 0 797613 0 193442 0 389238 0 760179 0 939905 0 872814 0 632627 0 283414 0 163161 0 853360 0 776860 0 149367 1 395269 0 981123 0 143626 0 648397 1 154982 0 330591 0 319232 0 531640 1 368050 1 253791 1 155724 0 824540 0 717392 0 965768 1 414779 0 428230 0 517240 0 469874 1 718428 0 620215 0 618659 0 649082 1 437573 0 964657 0 932502 0 434507 0 935277 0 756054 0 682387 0 456604 0 139872 0 354105 1 165485 0 515050 0 795686 1 395983 0 119513 0 217938 0 203914 0 565157 0 904191 0 419510 0 575000 1 120485 0 781181 0 299796 1 589749 0 854021 0 454086 0 139484 1 678849 0 346940 1 985436 0 237418 0 335780 0 491392 0 140880 0 962591 0 922565 0 288580 0 154280 0 425973 1 477177 0 648509 1 914815 0 249048 0 144323 0 651861 1 125324 0 398102 0 514065 1 391652 1 922167 1 442795 1 226330 0 134430 0 524230 1 438817 0 293794 0 868283 0 828890 0 882920 0 918777 1 212580 0 602410 0 976971 0 630226 0 171254 0 247116 0 505969 0 653864 1 586367 0 896890 0 650026 0 547744 0 598124 0 436126 1 739447 0 427484 0 218684 0 565564 1 743163 0 604614 1 509928 0 593390 0 970607 0 174701 0 529398 1 940942 0 442677 0 365364 0 114839 1 872734 0 267885 0 740505 1 629663 0 839884 0 241562 1 405533 0 667021 1 511621 0 476923 0 735822 0 492745 0 130930 1 261119 0 280709 0 898573 0 547802 0 600845 0 390381 0 629918 0 208298 0 513099 0 184938 0 187775 1 326322 1 146138 0 336047 0 532330 1 118137 0 212674 0 935596 0 737593 1 812025 0 168151 0 594739 0 843227 0 283925 0 475588 0 751905 1 226725 1 942504 0 395572 0 889883 0 818167 0 277767 0 842618 0 577810 0 873114 0 994538 0 727792 0 522506 0 367595 0 586104 0 424862 0 512813 0 356768 1 330506 0 779075 0 799501 0 987905 1 967756 0 830414 0 127313 1 786957 0 332892 0 448642 1 526039 0 444422 0 689500 0 806081 0 384618 0 756459 0 655787 0 419954 0 275092 0 515698 1 132871 0 714929 2 297816 0 426708 1 615047 0 771236 0 235869 1 931625 0 371635 1 427199 0 261315 0 582973 0 278091 0 153154 0 515217 1 860497 0 351741 0 403737 0 162004 0 740384 0 876714 0 951543 0 576723 0 391003 0 225865 0 984948 0 890328 0 803294 0 414913 0 414519 1 818413 0 487356 0 159768 0 865839 0 406567 1 623032 1 935442 1 106873 0 563878 0 620855 0 583169 0 337677 0 445973 0 156694 0 421940 1 613226 0 804410 1 553565 1 399524 0 331595 1 380067 0 701521 0 360770 1 958785 0 797934 0 883980 0 340614 0 435784 0 563837 0 200827 0 533941 1 265026 0 354481 0 566720 0 832746 0 386690 0 979285 1 594722 0 216738 0 369048 0 514424 0 954191 0 150181 0 388671 1 457244 1 206667 1 745200 0 412703 1 736771 0 347984 0 626074 1 218109 0 999435 0 858060 0 500384 0 903785 0 873859 0 204294 0 467106 1 357713 1 890026 0 751612 1 876680 0 756981 0 121439 1 411289 0 538466 1 932097 0 463727 0 552618 1 936638 0 348814 0 944102 0 689901 0 901083 0 396224 1 682178 0 596298 0 253005 0 985924 0 631565 0 630998 0 926665 0 302669 0 620020 0 439828 1 971295 0 165565 0 936543 0 296960 1 501692 0 525224 0 355085 0 830729 0 651948 0 424358 0 131478 0 268833 1 287489 0 808153 0 687639 1 497347 0 439660 0 847123 0 172307 0 810189 0 432068 0 903203 1 253085 0 180720 0 492224 0 411477 0 107181 0 312940 0 855186 1 373935 0 812989 1 993840 0 327856 0 506333 0 263159 1 372912 0 552788 0 722747 0 248467 0 955953 0 910622 0 137675 0 343421 1 413192 0 247801 0 171147 0 431283 0 461962 0 149467 0 758740 1 628337 0 574637 0 373600 1 930032 0 396590 0 238412 1 484321 0 795847 0 218456 1 792673 0 662256 0 971338 0 714738 0 753844 0 976645 1 918037 0 996253 0 373731 0 836272 0 167231 0 743330 0 807369 0 735307 0 789208 0 585324 0 498759 1 795004 0 203250 0 430794 0 156636 0 284143 1 740518 0 445289 0 599262 0 357949 1 493161 0 320251 0 231127 1 766193 1 555374 1 491484 0 925128 0 265093 0 267808 0 116735 0 963680 0 445694 0 215534 0 232854 0 168260 0 538955 0 243226 0 246435 0 582480 1 345539 0 924318 0 726880 0 190588 0 246705 0 619589 0 164988 1 729534 0 505014 0 920826 0 534982 1 110408 0 283052 0 840806 0 382394 0 876699 0 871432 0 379882 0 852002 1 372891 0 689034 1 743092 1 599174 0 421092 0 349658 1 811042 0 505316 0 116645 0 950880 0 788502 0 627486 0 498842 0 550294 0 328387 0 540152 0 385932 0 618682 0 550930 1 998192 0 663938 0 756870 0 337158 1 919875 0 315631 0 113464 0 556415 0 250249 1 403776 0 396002 0 976908 1 509489 1 485295 0 361829 0 603632 0 783494 1 439049 0 502634 0 378588 0 794731 0 641934 0 113516 0 425631 0 542245 0 512894 1 633090 0 464234 0 290162 0 638155 0 911429 1 106186 0 311783 0 528385 1 228403 0 209177 0 497929 0 735844 0 710741 0 276804 1 507545 1 485642 1 796375 0 171183 0 110084 0 714784 1 143924 0 996850 0 284834 0 830878 1 270208 0 407958 0 832300 0 927205 0 655356 0 831053 0 432740 0 893853 0 594988 0 979544 0 191891 0 831479 1 714346 0 326289 0 944537 0 779156 0 856153 0 473338 0 521694 1 136520 0 730819 0 912665 0 469966 0 952300 1 322609 0 890280 0 431583 0 155912 0 110143 0 808544 0 409074 0 824728 1 606037 0 636843 0 111874 0 439844 0 463513 1 577858 0 607351 0 682754 0 757352 0 307469 0 526296 0 658816 0 913337 0 488464 1 480094 1 263108 0 298412 1 261905 1 674485 1 223404 0 991480 0 804219 0 483088 1 100804 1 941807 1 593466 1 437442 0 942106 0 794951 0 182450 0 730973 0 687755 0 757644 0 998865 0 944953 1 386429 1 108270 0 205134 0 749325 0 774303 0 698470 0 719989 2 309323 0 444035 1 431478 0 797634 0 284836 1 238196 1 885789 0 287436 0 496067 1 206004 0 153027 0 469426 0 654974 0 943425 0 641845 1 794534 0 357808 0 536052 0 873384 1 790225 0 587498 0 639027 0 217899 0 589094 0 458829 0 626208 0 315041 0 283267 0 442494 0 159243 0 669800 0 520179 1 607974 0 465065 1 369941 0 447226 1 831668 0 922937 0 640474 0 153298 0 334749 0 221283 0 961496 0 804751 0 369226 0 691115 0 713172 1 621756 0 615116 0 947598 0 658002 0 374545 0 805806 1 235097 0 290971 0 180286 0 662088 0 884365 0 178081 0 507452 1 990624 0 892148 1 398683 0 605100 0 143109 0 230223 1 769602 0 420815 0 973546 1 608039 0 250162 0 786432 0 445195 0 938634 0 482495 0 796005 0 910604 0 327488 0 715202 0 648852 1 516959 1 984456 1 801331 0 786103 0 684193 0 247505 0 259792 0 185124 0 760700 0 362407 0 389525 0 179538 0 265437 0 266247 0 921851 0 488724 0 192524 0 338070 0 865607 0 963285 0 728491 0 553436 0 440616 0 463237 0 753452 0 920554 0 594783 0 725330 0 607259 0 979336 1 865201 0 140977 0 787351 1 272330 1 728025 2 804608 0 718829 1 482404 0 331170 0 753056 0 910365 1 379268 0 362843 0 135400 0 798579 0 250833 0 824116 0 322613 0 871305 0 488037 0 485813 1 886473 1 907113 0 833321 0 521592 0 254837 0 634499 0 574707 0 476839 0 149601 0 630683 0 500639 0 352120 0 569245 0 907012 0 700074 0 866805 0 951863 0 211578 1 357394 0 863749 0 596914 0 684653 0 528259 1 797636 0 326180 0 620075 0 965187 0 516182 0 728839 0 771509 0 264221 0 602704 0 672416 0 545506 0 777533 0 953334 1 369781 1 990998 0 982678 0 646069 0 331683 0 364055 0 521854 0 737252 0 344480 0 898519 0 957816 0 175960 0 489618 0 717044 0 101421 0 793948 0 737483 0 695117 0 167466 0 664732 1 143038 0 979963 0 467841 0 219028 0 130156 0 762951 0 376879 1 599031 0 676255 1 985446 0 884180 1 571462 0 815883 0 258265 0 569714 0 180008 0 633375 0 556538 0 669501 1 963761 0 753005 0 454758 0 698589 0 330119 0 164464 0 927354 0 231508 0 272910 0 305758 0 950542 0 291544 0 388616 0 577992 1 342830 0 491170 0 175553 0 439341 0 221186 0 868031 0 844117 0 744557 0 159536 0 727109 0 155604 0 608443 0 117862 0 991553 0 727443 0 378587 1 420948 0 457188 0 118236 0 987524 0 490596 1 524215 0 913464 0 398484 0 752504 0 449263 0 844007 0 686522 0 670142 0 607687 0 967713 0 291902 0 149839 1 840225 0 643226 1 535879 0 746630 1 598308 1 720356 1 724752 0 148498 1 110122 0 281388 0 728600 0 231548 1 531160 2 889003 0 193213 1 557218 1 125591 1 227244 0 791425 0 354455 0 601042 0 433663 0 471938 1 564654 0 645723 0 573572 0 437960 0 649800 0 544225 0 390256 1 488597 0 133889 1 931901 1 769475 0 844062 0 844129 0 732169 0 221854 0 776950 0 291006 0 845751 1 889764 0 929306 1 515457 0 556270 0 908935 0 525862 0 490514 0 774895 0 974522 0 669809 0 182953 0 836349 1 591269 0 550127 0 663190 1 109392 0 215278 0 674570 0 681486 0 941851 0 186934 0 918516 1 533940 1 556080 0 dtype: int64
fraud_df_up2=fraud_df_up.copy()
fraud_df_up2.shape
(1000, 38)
pred = fraud_df_up2.drop('fraud_reported',axis=1)
response = fraud_df_up2['fraud_reported']
fraud_con = pd.concat([pred,response],axis=1)
from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder,LabelEncoder
fraud_con["insured_sex"] = np.where(fraud_con["insured_sex"].str.contains("FEMALE"), 1, 0)
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), object(20) memory usage: 340.8+ KB
fraud_con.insured_education_level.value_counts()
JD 161 High School 160 Associate 145 MD 144 Masters 143 PhD 125 College 122 Name: insured_education_level, dtype: int64
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder()
fraud_con['insured_education_level'] = fraud_con['insured_education_level'].astype('category')
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null category 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: category(1), float64(5), int32(1), int64(12), object(19) memory usage: 334.3+ KB
fraud_con['insured_education_level'] = fraud_con['insured_education_level'].cat.codes
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(1), object(19) memory usage: 333.9+ KB
oe=OrdinalEncoder()
fraud_con.police_report_available.value_counts()
NO 343 Unknown 343 YES 314 Name: police_report_available, dtype: int64
fraud_con['police_report_available'] = fraud_con['police_report_available'].astype('category')
fraud_con['police_report_available'] = fraud_con['police_report_available'].cat.codes
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(2), object(18) memory usage: 327.1+ KB
fraud_con.auto_make.value_counts()
Suburu 80 Saab 80 Dodge 80 Nissan 78 Chevrolet 76 Ford 72 BMW 72 Toyota 70 Audi 69 Volkswagen 68 Accura 68 Jeep 67 Mercedes 65 Honda 55 Name: auto_make, dtype: int64
fraud_con.insured_education_level.value_counts()
3 161 2 160 0 145 4 144 5 143 6 125 1 122 Name: insured_education_level, dtype: int64
fraud_con['auto_make'] = fraud_con['auto_make'].astype('category')
fraud_con['auto_make'] = fraud_con['auto_make'].cat.codes
fraud_con.auto_make.value_counts()
11 80 10 80 4 80 9 78 3 76 5 72 2 72 12 70 1 69 13 68 0 68 7 67 8 65 6 55 Name: auto_make, dtype: int64
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(3), object(17) memory usage: 320.3+ KB
fraud_con.policy_state.value_counts()
OH 352 IL 338 IN 310 Name: policy_state, dtype: int64
fraud_con['policy_state'] = fraud_con['policy_state'].astype('category')
fraud_con['policy_state'] = fraud_con['policy_state'].cat.codes
fraud_con.policy_state.value_counts()
2 352 0 338 1 310 Name: policy_state, dtype: int64
fraud_con.policy_csl.value_counts()
250/500 351 100/300 349 500/1000 300 Name: policy_csl, dtype: int64
fraud_con['policy_csl'] = fraud_con['policy_csl'].astype('category')
fraud_con['policy_csl'] = fraud_con['policy_csl'].cat.codes
fraud_con.policy_csl.value_counts()
1 351 0 349 2 300 Name: policy_csl, dtype: int64
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(5), object(15) memory usage: 306.6+ KB
num=[]
cat=[]
for i in fraud_con.columns:
if fraud_con[i].dtype=='object':
cat.append(i)
else:
num.append(i)
cat
['policy_bind_date', 'insured_occupation', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'authorities_contacted', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'auto_model', 'fraud_reported']
fraud_con.insured_occupation.value_counts()
machine-op-inspct 93 prof-specialty 85 tech-support 78 sales 76 exec-managerial 76 craft-repair 74 transport-moving 72 other-service 71 priv-house-serv 71 armed-forces 69 adm-clerical 65 protective-serv 63 handlers-cleaners 54 farming-fishing 53 Name: insured_occupation, dtype: int64
fraud_con.insured_hobbies.value_counts()
reading 64 exercise 57 paintball 57 bungie-jumping 56 golf 55 camping 55 movies 55 kayaking 54 yachting 53 hiking 52 video-games 50 skydiving 49 base-jumping 49 board-games 48 polo 47 chess 46 dancing 43 sleeping 41 cross-fit 35 basketball 34 Name: insured_hobbies, dtype: int64
fraud_con.insured_relationship.value_counts()
own-child 183 other-relative 177 not-in-family 174 husband 170 wife 155 unmarried 141 Name: insured_relationship, dtype: int64
fraud_con.incident_severity.value_counts()
Minor Damage 354 Total Loss 280 Major Damage 276 Trivial Damage 90 Name: incident_severity, dtype: int64
fraud_con.authorities_contacted.value_counts()
Police 292 Fire 223 Other 198 Ambulance 196 None 91 Name: authorities_contacted, dtype: int64
fraud_con['authorities_contacted'] = fraud_con['authorities_contacted'].astype('category')
fraud_con['authorities_contacted'] = fraud_con['authorities_contacted'].cat.codes
fraud_con.authorities_contacted.value_counts()
4 292 1 223 3 198 0 196 2 91 Name: authorities_contacted, dtype: int64
fraud_con['insured_occupation'] = fraud_con['insured_occupation'].astype('category')
fraud_con['insured_occupation'] = fraud_con['insured_occupation'].cat.codes
fraud_con.insured_occupation.value_counts()
6 93 9 85 12 78 11 76 3 76 2 74 13 72 8 71 7 71 1 69 0 65 10 63 5 54 4 53 Name: insured_occupation, dtype: int64
num=[]
cat=[]
for i in fraud_con.columns:
if fraud_con[i].dtype=='object':
cat.append(i)
else:
num.append(i)
cat
['policy_bind_date', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'auto_model', 'fraud_reported']
fraud_con.incident_city.value_counts()
Springfield 157 Arlington 152 Columbus 149 Northbend 145 Hillsdale 141 Riverwood 134 Northbrook 122 Name: incident_city, dtype: int64
fraud_con['insured_hobbies'] = fraud_con['insured_hobbies'].astype('category')
fraud_con['insured_hobbies'] = fraud_con['insured_hobbies'].cat.codes
fraud_con.insured_hobbies.value_counts()
15 64 8 57 13 57 3 56 9 55 4 55 12 55 11 54 19 53 10 52 18 50 16 49 0 49 2 48 14 47 5 46 7 43 17 41 6 35 1 34 Name: insured_hobbies, dtype: int64
fraud_con.incident_city.value_counts()
Springfield 157 Arlington 152 Columbus 149 Northbend 145 Hillsdale 141 Riverwood 134 Northbrook 122 Name: incident_city, dtype: int64
fraud_con['incident_city'] = fraud_con['incident_city'].astype('category')
fraud_con['incident_city'] = fraud_con['incident_city'].cat.codes
fraud_con.incident_city.value_counts()
6 157 0 152 1 149 3 145 2 141 5 134 4 122 Name: incident_city, dtype: int64
fraud_con.incident_location.value_counts()
9043 Maple Hwy 1 7721 Washington Ridge 1 7930 Texas Ave 1 7002 Oak Hwy 1 3167 4th Ridge 1 5474 Weaver Hwy 1 5812 Oak St 1 1331 Elm Ridge 1 3808 5th Ave 1 6676 Tree Lane 1 7756 Pine Hwy 1 6128 Elm Lane 1 6256 Elm St 1 6684 Solo Lane 1 3656 Solo Ave 1 7574 4th St 1 8749 Tree St 1 6250 1st Ridge 1 8920 Best Ave 1 4955 Lincoln Ridge 1 8668 Flute St 1 8014 Embaracadero Drive 1 7819 Oak St 1 6501 5th Drive 1 3327 Lincoln Drive 1 7495 Washington Ave 1 2756 Britain Hwy 1 5769 Texas Lane 1 1386 Britain St 1 6859 Flute Ridge 1 4119 Texas St 1 1562 Britain St 1 6493 Lincoln Lane 1 7705 Lincoln Drive 1 8118 Elm Ridge 1 5640 Embaracadero Lane 1 3835 5th Ave 1 8167 Apache Ave 1 5997 Embaracadero Drive 1 4214 MLK Ridge 1 9878 Washington Ave 1 6011 Britain St 1 4782 Sky Lane 1 2376 Sky Ridge 1 5074 3rd St 1 1267 Francis Hwy 1 5985 Lincoln Lane 1 6724 Andromedia St 1 7900 Sky Hwy 1 7236 Apache Lane 1 9942 Tree Ave 1 1328 Texas Lane 1 5226 Maple St 1 1725 Solo Lane 1 3929 Elm Ave 1 5924 Maple Drive 1 3418 Texas Lane 1 7393 Washington St 1 9685 Sky Ridge 1 6604 Apache Drive 1 5532 Weaver Ridge 1 9240 Britain Ave 1 6428 Andromedia Lane 1 4931 Maple Drive 1 2774 Apache Drive 1 9562 4th Ridge 1 1298 Maple Hwy 1 7877 3rd Ridge 1 6303 1st Drive 1 5051 Elm St 1 6934 Lincoln Ave 1 2397 Cherokee Ave 1 7281 Maple Hwy 1 1229 5th Ave 1 6494 4th Ave 1 7745 Washington Ridge 1 1325 1st Lane 1 9397 Francis St 1 5862 Apache Ridge 1 8212 Flute Ridge 1 1654 Pine St 1 3743 Andromedia Ridge 1 6390 Apache St 1 8991 Texas Hwy 1 3771 4th St 1 3066 Francis Ave 1 4702 Texas Drive 1 7238 2nd St 1 5280 Pine Ave 1 9611 Pine Ridge 1 6206 3rd Ridge 1 5499 Elm Hwy 1 9224 Sky Drive 1 5778 Pine Ridge 1 1269 Flute Drive 1 9523 Solo Hwy 1 1376 Pine St 1 5771 Best St 1 7629 5th St 1 2253 Maple Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 4539 Texas St 1 4826 5th St 1 2311 4th St 1 2469 Francis Lane 1 3966 Oak Hwy 1 6874 Maple Ridge 1 5277 Texas Lane 1 4577 Sky Hwy 1 8233 Tree Drive 1 4291 Sky Hwy 1 1916 Elm St 1 1491 Francis Ridge 1 6329 Apache Ave 1 9760 Solo Lane 1 6853 Sky Hwy 1 1923 2nd Hwy 1 4335 1st St 1 8834 Elm Drive 1 4098 Weaver Ridge 1 2900 Sky Drive 1 3376 5th Drive 1 6981 Weaver St 1 7397 4th Drive 1 1992 Britain Drive 1 5667 4th Drive 1 1546 Cherokee Ave 1 4972 Francis Lane 1 4627 Elm Ridge 1 6738 Francis Hwy 1 3540 Maple St 1 9610 Cherokee St 1 4558 3rd Hwy 1 2644 MLK Drive 1 3419 Apache St 1 6331 MLK Ave 1 8946 2nd Drive 1 6384 5th Ridge 1 1643 Washington Hwy 1 2968 Andromedia Ave 1 4862 Lincoln Hwy 1 3900 Texas St 1 7897 Lincoln St 1 7529 Solo Ridge 1 3998 4th Hwy 1 5383 Maple Drive 1 4477 5th Ave 1 2603 Andromedia Hwy 1 1815 Cherokee Drive 1 1589 Pine St 1 8006 Maple Hwy 1 3110 Lincoln Lane 1 7649 Texas St 1 7846 Andromedia Drive 1 1833 Solo Ave 1 2014 Rock Ave 1 2003 Maple Hwy 1 8081 Flute Ridge 1 2980 Sky Ridge 1 5865 Sky Lane 1 1358 Maple St 1 6939 3rd Hwy 1 6677 Andromedia Drive 1 9573 Weaver Ave 1 3658 Rock Drive 1 8524 Pine Lane 1 7717 Britain Hwy 1 3926 Rock Lane 1 1126 Texas Hwy 1 6678 Weaver Drive 1 2457 Washington Ave 1 6117 4th Ave 1 4055 2nd Drive 1 9988 Rock Ridge 1 1123 5th Lane 1 8306 1st Drive 1 9875 MLK Ave 1 7197 2nd Drive 1 7168 Andromedia Ridge 1 9730 2nd Hwy 1 8617 Best Ave 1 9980 Lincoln Ave 1 8876 1st St 1 1012 5th Lane 1 1845 Best St 1 4116 Embaracadero Lane 1 2577 Washington Drive 1 1707 Sky Ave 1 6399 Oak Drive 1 1316 Britain Ridge 1 3094 Best Lane 1 6522 Apache Drive 1 5846 Weaver Drive 1 2526 Embaracadero Ave 1 6706 Francis Drive 1 4143 Maple Ridge 1 1957 Washington Ave 1 7954 Tree Ridge 1 3221 Solo Ridge 1 1135 Solo Lane 1 2230 1st St 1 7791 Britain Ridge 1 9489 3rd St 1 4545 4th Ridge 1 3089 Oak Ridge 1 2299 Britain Drive 1 7178 Best Drive 1 5224 5th Lane 1 9633 4th St 1 3726 MLK Hwy 1 6409 Cherokee Drive 1 9720 Lincoln Hwy 1 1472 4th Drive 1 2757 4th Hwy 1 7782 Rock St 1 7609 Rock St 1 6315 2nd Lane 1 9148 4th Hwy 1 2711 Britain Ave 1 6838 Flute Lane 1 7135 Flute Lane 1 8991 Embaracadero Ave 1 4546 Tree St 1 6357 Texas Lane 1 1331 Britain Hwy 1 9911 Britain Lane 1 8758 5th St 1 5650 Sky Drive 1 2123 MLK Ridge 1 4981 Weaver St 1 5894 Flute Drive 1 7523 Oak Lane 1 1617 Rock Drive 1 2063 Weaver St 1 7295 Tree Hwy 1 6608 Apache Lane 1 8489 Pine Hwy 1 4095 MLK St 1 3097 4th Drive 1 5780 4th Ave 1 8456 1st Ave 1 7709 Rock Lane 1 9818 Cherokee Ave 1 8064 4th Ave 1 7405 Oak St 1 8542 Lincoln Ridge 1 6738 Washington Hwy 1 8442 Britain Hwy 1 4981 Flute Hwy 1 4453 Best Ave 1 1687 3rd Lane 1 2306 5th Lane 1 4369 Maple Lane 1 1273 Rock Lane 1 6955 Pine Drive 1 8983 Francis Ridge 1 3177 MLK Ridge 1 5584 Britain Lane 1 9278 Francis Ridge 1 1810 Elm Hwy 1 3488 Flute Lane 1 4154 Lincoln Hwy 1 2646 MLK Drive 1 8204 Pine Lane 1 8940 Elm Ave 1 5269 Flute Hwy 1 2539 Embaracadero Ridge 1 9286 Oak Ave 1 7502 Rock Lane 1 4755 1st St 1 9573 2nd Ave 1 1240 Tree Lane 1 6259 Lincoln Hwy 1 2318 Washington Hwy 1 3416 Washington Drive 1 5994 5th Ave 1 4905 Francis Ave 1 5649 Texas Ave 1 5124 Maple St 1 2654 Embaracadero St 1 2509 Rock Drive 1 6492 4th Lane 1 4964 Elm Lane 1 8404 Embaracadero St 1 7783 Lincoln Hwy 1 3553 Texas Ave 1 7928 Maple Ridge 1 6259 Weaver St 1 7380 5th Hwy 1 8689 Maple Hwy 1 8704 Britain Lane 1 5455 Oak Hwy 1 3039 Oak Hwy 1 7066 Texas Ave 1 5211 Weaver Drive 1 5540 Sky St 1 4910 1st Lane 1 3171 Andromedia Lane 1 7162 Maple Ave 1 6429 4th Hwy 1 5191 4th St 1 7534 MLK Hwy 1 5969 Francis St 1 4793 4th Ridge 1 6751 5th Hwy 1 8548 Cherokee Ridge 1 4907 Andromedia Drive 1 8949 Rock Hwy 1 1540 Apache Lane 1 9325 Lincoln Drive 1 5352 Lincoln Drive 1 8862 Maple Ridge 1 3590 Best Hwy 1 7459 Flute St 1 8832 Pine Drive 1 5483 Francis Drive 1 5532 Francis Lane 1 9639 Britain Ridge 1 8995 1st Ave 1 7828 Cherokee Ave 1 2920 5th Ave 1 2100 MLK St 1 8509 Apache St 1 4629 Elm Ridge 1 3457 Texas Lane 1 1941 5th Ridge 1 6149 Best Ridge 1 3936 Tree Drive 1 6317 Best St 1 4429 Washington St 1 4618 Flute Ave 1 2293 Washington Ave 1 2299 1st St 1 3797 Solo Lane 1 6848 Elm Hwy 1 1186 Rock St 1 5719 2nd Lane 1 1515 Pine Lane 1 7705 Best Ridge 1 2809 Francis Lane 1 8215 Flute Drive 1 3790 Andromedia Hwy 1 2889 Francis St 1 5431 3rd Ridge 1 3998 Flute St 1 8941 Solo Ridge 1 7460 Apache Lane 1 9169 Pine Ridge 1 7281 Oak St 1 8701 5th Lane 1 9138 1st St 1 3550 Washington Ave 1 2003 2nd Hwy 1 4434 Weaver St 1 4279 Solo Drive 1 2644 Elm Drive 1 2862 Tree Ridge 1 9109 Britain Drive 1 6456 Andromedia Drive 1 9816 Britain St 1 9879 Apache Drive 1 5189 Francis Drive 1 2914 Oak Drive 1 4876 Washington Drive 1 7426 Rock Drive 1 8973 Washington St 1 1832 Elm Hwy 1 3525 3rd Hwy 1 2654 Elm Drive 1 1679 2nd Hwy 1 4158 Washington Lane 1 3878 Tree Lane 1 3282 4th Lane 1 3184 Oak Ave 1 2696 Cherokee Ridge 1 5279 Pine Ridge 1 7316 Texas Ave 1 7201 Washington Ave 1 1381 Francis Ave 1 8718 Apache Lane 1 1416 Cherokee Ridge 1 1580 Maple Lane 1 8269 Sky Hwy 1 3323 1st Lane 1 9751 Sky Ridge 1 3092 Texas Drive 1 4814 Lincoln Lane 1 1681 Cherokee Hwy 1 8845 5th Ave 1 8821 Elm St 1 2725 Britain Ridge 1 9588 Solo St 1 6618 Cherokee Drive 1 7511 1st Ave 1 6583 MLK Ridge 1 4460 4th Lane 1 7571 Elm Ridge 1 3693 Pine Ave 1 3660 Andromedia Hwy 1 6605 Tree Ave 1 6067 Weaver Ridge 1 3227 Maple Ave 1 8667 Weaver Lane 1 9397 5th Hwy 1 4671 5th Ridge 1 1589 Best Ave 1 6851 3rd Drive 1 8080 Oak Lane 1 9214 Elm Ridge 1 4434 Lincoln Ave 1 6723 Best Drive 1 2117 Lincoln Hwy 1 8983 Tree St 1 4347 2nd Ridge 1 9418 5th Hwy 1 8655 Cherokee Lane 1 4937 Flute Drive 1 7240 5th Ridge 1 5585 Washington Drive 1 5743 4th Ridge 1 1371 Texas Lane 1 9529 4th Drive 1 9316 Pine Ave 1 1741 Best Ridge 1 8586 1st Ridge 1 2537 5th Ave 1 5914 Oak Ave 1 8624 Francis Ave 1 5771 Sky Ave 1 5868 Best Drive 1 8203 Lincoln Ave 1 7773 Tree Hwy 1 1173 Andromedia Ave 1 6484 Tree Drive 1 6741 Oak Ridge 1 9488 Best Drive 1 6378 Britain Ave 1 5782 Rock Drive 1 2820 Britain St 1 9580 MLK Ave 1 2201 4th Lane 1 9856 Apache St 1 6165 Rock Ridge 1 5260 Francis Drive 1 5480 3rd Ridge 1 4642 Rock Ridge 1 4188 Britain Ave 1 5838 Pine Lane 1 8621 Best Ridge 1 8906 Elm Lane 1 4585 Francis Ave 1 5839 Weaver Lane 1 9369 Flute Hwy 1 5586 2nd St 1 4254 Best Ridge 1 3053 Lincoln Drive 1 3397 5th Ave 1 1951 Best Ave 1 7535 5th Lane 1 8917 Cherokee Lane 1 1536 Flute Drive 1 1028 Sky Lane 1 2668 Cherokee St 1 6717 Best Drive 1 1806 Weaver Ridge 1 8477 Francis Hwy 1 8907 Tree Ave 1 2903 Weaver Drive 1 9929 Rock Drive 1 6193 1st Hwy 1 6092 5th Ave 1 9293 Pine Lane 1 4175 Elm Ridge 1 7144 Andromedia St 1 7121 Rock St 1 6945 Texas Hwy 1 1578 5th Lane 1 8049 4th St 1 2804 Best St 1 4496 Pine Lane 1 6681 Texas Ridge 1 5249 4th Ave 1 1620 Oak Ave 1 4625 MLK Drive 1 6435 Texas Ave 1 9760 4th Hwy 1 3966 Francis Ridge 1 4985 Sky Lane 1 2275 Best Lane 1 7500 Texas Ridge 1 1929 Britain Drive 1 5663 Oak Lane 1 8770 1st Lane 1 2886 Tree Ridge 1 3041 3rd Ave 1 6467 Best Ave 1 8811 Maple Hwy 1 8538 Texas Lane 1 5855 Apache St 1 2849 Pine Drive 1 5765 Washington St 1 9439 MLK St 1 3122 Apache Drive 1 6515 Oak Lane 1 3846 4th Hwy 1 7576 Pine Ridge 1 2078 3rd Ave 1 4414 Solo Drive 1 3639 Flute Hwy 1 8766 Lincoln Lane 1 3443 Maple Ridge 1 7253 MLK St 1 8964 Francis St 1 4857 Weaver St 1 6702 Andromedia St 1 6309 5th Ave 1 2577 Texas Ridge 1 3555 Francis Ridge 1 6957 Weaver Drive 1 5007 Oak St 1 9358 Texas Ridge 1 1346 5th Lane 1 7859 4th Ridge 1 1762 Maple Hwy 1 5022 1st St 1 6638 Tree Drive 1 3818 Texas Ridge 1 4687 5th Drive 1 3884 Pine Lane 1 7570 Cherokee Drive 1 9066 Best Ridge 1 5178 Weaver Hwy 1 8188 Tree Ave 1 1655 Francis Hwy 1 1555 Washington Lane 1 4431 Rock St 1 6634 Texas Ridge 1 3982 Washington Hwy 1 7582 Pine Drive 1 5812 Weaver Ave 1 5058 4th Lane 1 5790 Flute Ridge 1 7082 Oak Ridge 1 6440 Rock Lane 1 5630 1st Drive 1 9737 Solo Hwy 1 1598 3rd Drive 1 9910 Maple Ave 1 7042 Maple Ridge 1 5312 Francis Ridge 1 3220 Rock Drive 1 2381 1st Hwy 1 7973 4th St 1 6158 Sky Ridge 1 9138 3rd St 1 9239 Washington Ridge 1 5868 Sky Hwy 1 5071 1st Lane 1 8097 Maple Lane 1 8576 Andromedia St 1 9417 Tree Hwy 1 3770 Flute Drive 1 7601 Andromedia Lane 1 8618 Texas Lane 1 3753 Francis Lane 1 7551 Britain Lane 1 5499 Flute Ridge 1 4721 Cherokee Hwy 1 6985 Maple Lane 1 8021 Flute Ave 1 4574 Britain Hwy 1 9847 Elm St 1 8353 Britain Ridge 1 4053 Sky Lane 1 4653 Pine St 1 6550 Andromedia St 1 6719 Flute St 1 5806 Embaracadero St 1 3127 Flute St 1 1213 4th Lane 1 7331 Sky Hwy 1 4983 MLK Ridge 1 6619 Flute Ave 1 5650 Rock Ave 1 7476 4th St 1 8957 Weaver Drive 1 8245 4th Hwy 1 4475 Lincoln Ridge 1 7121 Francis Lane 1 3029 5th Ave 1 2333 Maple Lane 1 3872 5th Drive 1 6536 MLK Hwy 1 2324 Texas Ridge 1 1218 Sky Hwy 1 6770 1st St 1 5053 Tree Drive 1 3982 Weaver Lane 1 9153 3rd Hwy 1 8602 Washington Ridge 1 9603 Texas Lane 1 9422 Washington Ridge 1 3486 Flute Ave 1 6355 4th Hwy 1 6971 Best Ridge 1 4519 Embaracadero St 1 9728 Britain Hwy 1 3263 Pine Ridge 1 2430 MLK Ave 1 7041 Tree Ridge 1 2651 MLK Lane 1 2086 Francis Drive 1 4995 Weaver Ridge 1 4939 Oak Lane 1 5608 Solo St 1 1275 4th Ridge 1 2832 Andromedia Lane 1 4699 Texas Ridge 1 7628 4th Lane 1 6530 Weaver Ave 1 3659 Oak Lane 1 7488 Lincoln Lane 1 9742 5th Ridge 1 3929 Oak Drive 1 7877 Sky Lane 1 7923 Elm Ave 1 3799 Embaracadero Drive 1 4237 4th St 1 6834 1st Drive 1 7466 MLK Ridge 1 3423 Francis Ave 1 1110 4th Drive 1 1738 Solo Lane 1 3098 Oak Lane 1 4492 Andromedia Ave 1 3842 Solo Ridge 1 2942 1st Lane 1 7756 Solo Drive 1 8872 Oak Ridge 1 4939 Best St 1 2220 1st Lane 1 2787 MLK St 1 3796 Cherokee Drive 1 4835 Britain Ridge 1 9657 5th Ave 1 5971 5th Hwy 1 4232 Britain Ridge 1 6260 5th Lane 1 8805 Cherokee Drive 1 3805 Lincoln Hwy 1 1558 1st Ridge 1 3925 Sky St 1 6751 Pine Ridge 1 6104 Oak Ave 1 5418 Britain Ave 1 2733 Texas Drive 1 1215 Pine Hwy 1 3414 Elm Ave 1 6581 Rock Ridge 1 3654 Cherokee Ave 1 3340 3rd Hwy 1 6668 Andromedia Ridge 1 8638 3rd Ave 1 5104 Francis Drive 1 9787 Andromedia Ave 1 5639 1st Ridge 1 9706 MLK Lane 1 2352 MLK Drive 1 9360 3rd Drive 1 3439 Andromedia Hwy 1 9105 Tree Lane 1 2048 3rd Ridge 1 5061 Francis Ave 1 8281 Lincoln Lane 1 6044 Weaver Drive 1 4710 Lincoln Hwy 1 1879 4th Lane 1 9734 2nd Ridge 1 1133 Apache St 1 7447 Lincoln Ridge 1 9918 Andromedia Drive 1 1306 Andromedia St 1 3777 Maple Ave 1 8453 Elm St 1 5455 Tree Ridge 1 9794 Embaracadero St 1 7434 Oak Hwy 1 6516 Solo Drive 1 8879 1st Drive 1 3087 Oak Hwy 1 2199 Texas Drive 1 2204 Washington Lane 1 1437 3rd Lane 1 9744 Texas Drive 1 2865 Maple Lane 1 1699 Oak Drive 1 9236 2nd Hwy 1 4538 Flute Hwy 1 7780 Flute Lane 1 1529 Elm Ridge 1 2617 Andromedia Drive 1 9070 Tree Ave 1 3311 2nd Drive 1 7121 Britain Drive 1 6068 2nd St 1 5201 Texas Hwy 1 1914 Francis St 1 9169 Cherokee Hwy 1 5506 Best St 1 1248 MLK Ridge 1 6731 Andromedia Hwy 1 9020 Elm Ave 1 6058 Andromedia Hwy 1 8492 Weaver Hwy 1 3567 4th Drive 1 1030 Pine Lane 1 6479 Francis Ave 1 5380 Pine St 1 6443 Washington Ridge 1 2352 Sky Drive 1 5459 MLK Ave 1 2889 Weaver St 1 3618 Maple Lane 1 4272 Oak Ridge 1 2272 Embaracadero Drive 1 7223 Embaracadero St 1 3102 Apache St 1 1919 4th Lane 1 1618 Maple Hwy 1 3196 Cherokee St 1 3061 Francis Hwy 1 7428 Sky Hwy 1 6110 Rock Ridge 1 2823 Weaver Lane 1 2950 MLK Ave 1 3492 Flute Lane 1 9633 Rock Hwy 1 4231 3rd Ave 1 2193 4th Ridge 1 7069 4th Hwy 1 4885 Oak Lane 1 7693 Cherokee Lane 1 8639 5th Hwy 1 4242 Rock Lane 1 9317 Apache Ave 1 1515 Embaracadero St 1 9103 MLK Lane 1 4390 4th Drive 1 5071 Flute Ridge 1 7976 Britain Drive 1 7583 Washington Ave 1 6603 Francis Hwy 1 1102 Apache Hwy 1 5678 Lincoln Drive 1 7819 2nd Ave 1 9081 Cherokee Hwy 1 3167 2nd St 1 4693 Lincoln Hwy 1 6451 1st Hwy 1 1220 MLK Ave 1 7909 Andromedia Hwy 1 4652 Flute Drive 1 7504 Flute Drive 1 1364 Best St 1 1388 Embaracadero Hwy 1 5874 1st Hwy 1 5341 5th Ave 1 6137 MLK St 1 3289 Britain Drive 1 8782 3rd St 1 1830 Sky St 1 4994 Lincoln Drive 1 8492 Andromedia Ridge 1 9082 3rd Lane 1 5779 2nd Lane 1 1365 Francis Ave 1 4176 Britain Hwy 1 1087 Flute Drive 1 7835 Cherokee Hwy 1 3930 Embaracadero St 1 1880 Weaver Drive 1 2037 5th Drive 1 3841 Washington Lane 1 5236 Weaver Drive 1 9101 2nd Hwy 1 4872 Rock Ridge 1 1953 Sky Lane 1 3664 Francis Ridge 1 8864 Tree Ridge 1 2798 1st Ave 1 8822 Sky St 1 7701 Tree St 1 7785 Lincoln Lane 1 7552 3rd St 1 7327 Lincoln Drive 1 9352 Washington Ave 1 1469 Lincoln Drive 1 1910 Sky Ave 1 3769 Sky St 1 4866 4th Hwy 1 7684 Francis Ridge 1 8100 3rd Ave 1 8926 Texas Ridge 1 4633 5th Lane 1 3809 Texas Lane 1 9177 Texas Ave 1 8214 Flute St 1 8579 Apache Drive 1 3288 Tree Lane 1 4780 Best Drive 1 4614 MLK Ave 1 5901 Elm Drive 1 4755 Best Lane 1 3903 Oak Ave 1 5783 Oak Ave 1 6179 3rd Ridge 1 7314 Tree Drive 1 2808 Elm St 1 4020 Best Drive 1 9821 Francis Ave 1 3104 Sky Drive 1 7112 Weaver Ave 1 7740 MLK St 1 1532 Washington St 1 9751 Tree St 1 4554 Sky Ave 1 6191 Oak Lane 1 9038 2nd Lane 1 5318 5th Ave 1 5445 Tree Hwy 1 9719 4th Lane 1 9245 Weaver Ridge 1 8198 Embaracadero Lane 1 9279 Oak Hwy 1 2280 4th Ave 1 1353 Washington St 1 1553 Lincoln St 1 8459 Apache Ave 1 5276 2nd Lane 1 5602 Britain St 1 3618 Sky Ave 1 3592 MLK Ridge 1 3492 Britain St 1 7098 Lincoln Hwy 1 3995 Lincoln Hwy 1 1989 Solo Lane 1 2777 Solo Drive 1 9935 4th Drive 1 4567 Pine Ave 1 9034 Weaver Ridge 1 3100 Best St 1 3495 Britain Drive 1 6375 2nd Lane 1 8897 Sky St 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 3653 Elm Drive 1 1985 5th Ave 1 6408 Weaver Ridge 1 3172 Tree Ridge 1 6658 Weaver St 1 2986 MLK Drive 1 2100 Francis Drive 1 2878 Britain Hwy 1 3006 Lincoln Ridge 1 6582 Elm Lane 1 5925 Tree Hwy 1 2697 Oak Drive 1 8954 Apache Lane 1 4615 Embaracadero Ave 1 7825 1st Ridge 1 2290 4th Ave 1 3952 Andromedia Lane 1 6574 4th Drive 1 1507 Solo Ave 1 3915 Embaracadero St 1 2753 Cherokee Ave 1 5333 MLK Lane 1 1422 Flute Ave 1 8101 3rd Ridge 1 5821 2nd St 1 4905 Best Lane 1 4447 Francis Hwy 1 3706 4th Hwy 1 8809 Flute St 1 1840 Embaracadero Ave 1 3847 Elm Hwy 1 5621 4th Ave 1 3508 Washington St 1 4058 Tree Drive 1 2500 Tree St 1 3706 Texas Hwy 1 6608 MLK Hwy 1 5093 Flute Lane 1 3834 Pine St 1 6309 Cherokee Ave 1 2123 Texas Ave 1 8078 Britain Hwy 1 2492 Lincoln Lane 1 5168 5th Ave 1 2005 Texas Hwy 1 1818 Tree St 1 7108 Tree St 1 8211 Sky Hwy 1 7797 Tree Ridge 1 7061 Cherokee Drive 1 8096 Apache Hwy 1 5363 Weaver Lane 1 1512 Rock Lane 1 3814 Britain Drive 1 5812 3rd Hwy 1 8368 Cherokee Ave 1 4296 Pine Hwy 1 7575 Pine St 1 2337 Lincoln Hwy 1 9322 Rock Hwy 1 9007 Francis Hwy 1 8336 1st Ridge 1 2873 Flute Ave 1 9214 Texas Drive 1 4394 Oak St 1 8150 Washington Ridge 1 5622 Best Ridge 1 6045 Andromedia St 1 2905 Embaracadero Drive 1 7544 Washington Ave 1 7155 Apache Drive 1 6359 MLK Ridge 1 4965 MLK Drive 1 1091 1st Drive 1 8381 Solo Hwy 1 4234 Cherokee Lane 1 7299 Apache St 1 7733 Britain Lane 1 1705 Weaver St 1 6956 Maple Drive 1 9798 Sky Ridge 1 3707 Oak Ridge 1 5028 Maple Ridge 1 6035 Rock Ave 1 3246 Britain Ridge 1 9067 Texas Ave 1 4672 MLK St 1 7630 Rock Drive 1 4107 MLK Ridge 1 7644 Tree Ridge 1 3028 5th St 1 9633 MLK Lane 1 6931 Elm St 1 5475 Rock Lane 1 1454 5th Ridge 1 3275 Pine St 1 9484 Pine Drive 1 4264 Lincoln Ridge 1 3422 Flute St 1 8212 Rock Ave 1 3447 Solo Ave 1 2215 Best Ave 1 9373 Pine Hwy 1 4268 2nd Ave 1 1956 Apache St 1 1628 Best Drive 1 2087 Apache Ave 1 8125 Texas Ridge 1 3320 5th Hwy 1 9724 Maple St 1 2494 Andromedia Drive 1 8917 Tree Ridge 1 9078 Francis Ridge 1 6889 Cherokee St 1 7816 MLK Lane 1 9154 MLK Hwy 1 3052 Weaver Ridge 1 3751 Tree Hwy 1 5160 2nd Hwy 1 1320 Flute Lane 1 7615 Weaver Drive 1 6655 5th Drive 1 6888 Elm Ridge 1 7693 Britain Lane 1 2217 Tree Lane 1 8742 4th St 1 4239 Weaver Ave 1 8085 Andromedia St 1 2533 Elm St 1 1824 5th Lane 1 1809 Sky St 1 9748 Sky Drive 1 6660 MLK Drive 1 2850 Washington St 1 8493 Apache Drive 1 4486 Cherokee Ridge 1 2289 Weaver Ridge 1 7477 MLK Drive 1 3697 Apache Drive 1 7142 5th Lane 1 9682 Cherokee Ridge 1 5904 1st Drive 1 1128 Maple Lane 1 Name: incident_location, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 12-10-1995 1 11-09-2010 1 25-01-2008 1 28-10-1998 1 11-03-2014 1 25-12-2001 1 10-11-1992 1 28-01-1992 1 06-05-2002 1 07-02-2007 1 01-10-2010 1 10-02-1997 1 19-12-2004 1 29-07-1995 1 15-05-2000 1 04-03-2006 1 18-01-2014 1 21-05-2000 1 19-05-1990 1 24-09-1992 1 05-08-2014 1 22-02-1992 1 22-06-2009 1 02-12-2011 1 18-02-1995 1 28-01-1993 1 24-06-1998 1 27-05-1994 1 26-06-2009 1 01-03-1997 1 29-11-1999 1 05-12-2013 1 13-05-2001 1 16-09-2013 1 16-09-1990 1 25-05-2002 1 09-11-1992 1 13-07-2013 1 08-05-2005 1 26-08-2000 1 07-12-2001 1 22-03-1992 1 26-09-2010 1 17-05-1994 1 04-06-1996 1 22-10-2011 1 19-05-1992 1 09-04-2001 1 04-05-1995 1 22-07-2004 1 17-02-2009 1 08-04-1996 1 11-12-1992 1 21-06-1994 1 01-10-2001 1 29-09-2001 1 11-07-2007 1 29-02-2004 1 13-04-2006 1 01-10-2006 1 29-01-1993 1 28-05-2014 1 19-12-1997 1 04-02-1992 1 10-10-2005 1 21-09-1990 1 26-11-2001 1 28-10-1997 1 20-11-2005 1 05-07-2007 1 14-04-1993 1 03-01-2015 1 18-06-1997 1 28-04-2013 1 03-11-2009 1 07-10-1990 1 19-06-2008 1 21-12-1999 1 05-02-1997 1 21-12-1996 1 10-02-2002 1 12-11-2009 1 28-09-2007 1 07-07-2013 1 11-10-2012 1 04-01-2009 1 10-02-2008 1 14-02-1994 1 10-03-1997 1 03-08-1994 1 08-04-1991 1 31-05-2004 1 21-02-2006 1 22-07-1999 1 11-07-2002 1 20-02-2009 1 18-10-2000 1 16-07-2003 1 07-03-2005 1 02-12-2012 1 02-06-2012 1 21-12-2013 1 20-12-1990 1 01-05-1997 1 25-03-2007 1 03-02-1994 1 02-04-1991 1 23-02-2014 1 20-02-1998 1 19-06-1996 1 30-09-1993 1 10-02-2007 1 08-10-1995 1 09-10-2009 1 23-04-2008 1 09-02-2007 1 16-01-2013 1 09-08-2003 1 17-04-2005 1 15-07-2011 1 17-12-1995 1 30-10-1997 1 14-03-1990 1 16-02-2001 1 06-06-2001 1 19-04-2009 1 19-12-1998 1 19-05-1993 1 13-04-1991 1 01-11-2014 1 22-07-2002 1 29-04-2010 1 27-09-2011 1 16-11-2007 1 02-08-1991 1 26-09-1991 1 21-01-2002 1 08-02-1990 1 17-08-1991 1 06-09-2005 1 27-01-1991 1 18-03-1990 1 25-08-1990 1 13-06-1992 1 11-10-1994 1 27-10-2013 1 03-03-1992 1 09-05-2009 1 08-07-2001 1 09-07-2013 1 26-10-2012 1 26-10-1996 1 12-05-2007 1 24-06-2014 1 27-04-2009 1 11-10-1992 1 20-08-1994 1 02-10-2003 1 18-06-2011 1 28-08-2005 1 23-01-2013 1 09-08-1990 1 29-07-1996 1 11-05-1996 1 04-01-1996 1 08-02-1996 1 20-09-1999 1 19-06-1992 1 17-03-2010 1 21-05-2010 1 12-01-2012 1 31-01-2011 1 19-01-1998 1 16-05-2001 1 02-02-1996 1 29-05-1992 1 04-03-2007 1 23-07-2014 1 12-04-2013 1 10-10-1993 1 08-05-2001 1 03-02-1993 1 27-04-2012 1 11-08-2007 1 19-10-1990 1 16-07-1995 1 07-12-1993 1 22-09-2002 1 06-06-1993 1 19-10-1992 1 24-04-2013 1 16-06-1993 1 10-06-2014 1 23-11-1997 1 01-12-2008 1 15-08-1999 1 23-04-1995 1 16-07-1991 1 29-04-1990 1 29-05-2003 1 02-06-2002 1 15-10-2005 1 12-10-2006 1 04-03-2001 1 19-09-1991 1 02-09-1996 1 24-01-2010 1 30-04-2007 1 28-02-2010 1 21-11-2006 1 15-09-1990 1 07-09-2006 1 08-12-2001 1 15-02-2011 1 14-06-2004 1 28-03-1990 1 15-04-2004 1 04-03-2014 1 23-06-2000 1 03-06-2009 1 17-11-2004 1 07-04-1998 1 08-04-2003 1 03-07-1992 1 16-11-2004 1 23-01-1996 1 16-11-2000 1 21-08-2006 1 12-12-1991 1 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25-05-1990 2 07-04-1999 2 09-07-2002 2 15-11-1997 2 03-02-1997 2 04-05-2000 2 20-07-1991 2 21-09-2005 2 08-11-2009 2 04-06-2000 2 24-06-1990 2 07-07-1996 2 28-01-2010 2 15-05-1997 2 22-08-1991 2 06-05-2007 2 07-12-1999 2 19-09-1995 2 30-08-1993 2 09-03-2003 2 16-07-2002 2 07-12-1995 2 14-07-1997 2 03-01-2004 2 07-11-1997 2 29-09-1999 2 28-04-1992 3 05-08-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INSURED_RELATIONSHIP : 6 unmarried 141 wife 155 husband 170 not-in-family 174 other-relative 177 own-child 183 Name: insured_relationship, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 19-02-2015 10 11-02-2015 10 07-02-2015 10 10-02-2015 10 25-01-2015 10 26-01-2015 11 29-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 03-02-2015 13 23-01-2015 13 09-02-2015 13 27-01-2015 13 22-01-2015 14 20-02-2015 14 27-02-2015 14 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-01-2015 15 05-02-2015 16 16-02-2015 16 15-02-2015 16 16-01-2015 16 13-02-2015 16 09-01-2015 17 26-02-2015 17 08-02-2015 17 24-02-2015 17 06-01-2015 17 03-01-2015 18 18-01-2015 18 01-02-2015 18 28-02-2015 18 14-02-2015 18 25-02-2015 18 20-01-2015 18 21-02-2015 19 23-02-2015 19 01-01-2015 19 14-01-2015 19 12-01-2015 19 21-01-2015 19 22-02-2015 20 06-02-2015 20 12-02-2015 20 31-01-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 24-01-2015 24 10-01-2015 24 04-02-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_TYPE : 4 Parked Car 84 Vehicle Theft 94 Single Vehicle Collision 403 Multi-vehicle Collision 419 Name: incident_type, dtype: int64 COLLISION_TYPE : 4 Unknown 178 Front Collision 254 Side Collision 276 Rear Collision 292 Name: collision_type, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 9043 Maple Hwy 1 3414 Elm Ave 1 6581 Rock Ridge 1 3654 Cherokee Ave 1 3340 3rd Hwy 1 6668 Andromedia Ridge 1 8638 3rd Ave 1 5104 Francis Drive 1 9787 Andromedia Ave 1 5639 1st Ridge 1 9706 MLK Lane 1 2352 MLK Drive 1 9360 3rd Drive 1 3439 Andromedia Hwy 1 9105 Tree Lane 1 2048 3rd Ridge 1 5061 Francis Ave 1 8281 Lincoln Lane 1 6044 Weaver Drive 1 4710 Lincoln Hwy 1 1879 4th Lane 1 9734 2nd Ridge 1 1133 Apache St 1 7447 Lincoln Ridge 1 9918 Andromedia Drive 1 1306 Andromedia St 1 3777 Maple Ave 1 8453 Elm St 1 1215 Pine Hwy 1 5455 Tree Ridge 1 2733 Texas Drive 1 6104 Oak Ave 1 3799 Embaracadero Drive 1 4237 4th St 1 6834 1st Drive 1 7466 MLK Ridge 1 3423 Francis Ave 1 1110 4th Drive 1 1738 Solo Lane 1 3098 Oak Lane 1 4492 Andromedia Ave 1 3842 Solo Ridge 1 2942 1st Lane 1 7756 Solo Drive 1 8872 Oak Ridge 1 4939 Best St 1 2220 1st Lane 1 2787 MLK St 1 3796 Cherokee Drive 1 4835 Britain Ridge 1 9657 5th Ave 1 5971 5th Hwy 1 4232 Britain Ridge 1 6260 5th Lane 1 8805 Cherokee Drive 1 3805 Lincoln Hwy 1 1558 1st Ridge 1 3925 Sky St 1 6751 Pine Ridge 1 5418 Britain Ave 1 9794 Embaracadero St 1 7434 Oak Hwy 1 6516 Solo Drive 1 5459 MLK Ave 1 2889 Weaver St 1 3618 Maple Lane 1 4272 Oak Ridge 1 2272 Embaracadero Drive 1 7223 Embaracadero St 1 3102 Apache St 1 1919 4th Lane 1 1618 Maple Hwy 1 3196 Cherokee St 1 3061 Francis Hwy 1 7428 Sky Hwy 1 6110 Rock Ridge 1 2823 Weaver Lane 1 2950 MLK Ave 1 3492 Flute Lane 1 9633 Rock Hwy 1 4231 3rd Ave 1 2193 4th Ridge 1 7069 4th Hwy 1 4885 Oak Lane 1 7693 Cherokee Lane 1 8639 5th Hwy 1 4242 Rock Lane 1 9317 Apache Ave 1 1515 Embaracadero St 1 9103 MLK Lane 1 2352 Sky Drive 1 6443 Washington Ridge 1 5380 Pine St 1 6479 Francis Ave 1 8879 1st Drive 1 3087 Oak Hwy 1 2199 Texas Drive 1 2204 Washington Lane 1 1437 3rd Lane 1 9744 Texas Drive 1 2865 Maple Lane 1 1699 Oak Drive 1 9236 2nd Hwy 1 4538 Flute Hwy 1 7780 Flute Lane 1 1529 Elm Ridge 1 2617 Andromedia Drive 1 7923 Elm Ave 1 9070 Tree Ave 1 7121 Britain Drive 1 6068 2nd St 1 5201 Texas Hwy 1 1914 Francis St 1 9169 Cherokee Hwy 1 5506 Best St 1 1248 MLK Ridge 1 6731 Andromedia Hwy 1 9020 Elm Ave 1 6058 Andromedia Hwy 1 8492 Weaver Hwy 1 3567 4th Drive 1 1030 Pine Lane 1 3311 2nd Drive 1 4390 4th Drive 1 7877 Sky Lane 1 9742 5th Ridge 1 8188 Tree Ave 1 1655 Francis Hwy 1 1555 Washington Lane 1 4431 Rock St 1 6634 Texas Ridge 1 3982 Washington Hwy 1 7582 Pine Drive 1 5812 Weaver Ave 1 5058 4th Lane 1 5790 Flute Ridge 1 7082 Oak Ridge 1 6440 Rock Lane 1 5630 1st Drive 1 9737 Solo Hwy 1 1598 3rd Drive 1 9910 Maple Ave 1 7042 Maple Ridge 1 5312 Francis Ridge 1 3220 Rock Drive 1 2381 1st Hwy 1 7973 4th St 1 6158 Sky Ridge 1 9138 3rd St 1 9239 Washington Ridge 1 5868 Sky Hwy 1 5071 1st Lane 1 8097 Maple Lane 1 5178 Weaver Hwy 1 8576 Andromedia St 1 9066 Best Ridge 1 3884 Pine Lane 1 9439 MLK St 1 3122 Apache Drive 1 6515 Oak Lane 1 3846 4th Hwy 1 7576 Pine Ridge 1 2078 3rd Ave 1 4414 Solo Drive 1 3639 Flute Hwy 1 8766 Lincoln Lane 1 3443 Maple Ridge 1 7253 MLK St 1 8964 Francis St 1 4857 Weaver St 1 6702 Andromedia St 1 6309 5th Ave 1 2577 Texas Ridge 1 3555 Francis Ridge 1 6957 Weaver Drive 1 5007 Oak St 1 9358 Texas Ridge 1 1346 5th Lane 1 7859 4th Ridge 1 1762 Maple Hwy 1 5022 1st St 1 6638 Tree Drive 1 3818 Texas Ridge 1 4687 5th Drive 1 7570 Cherokee Drive 1 9417 Tree Hwy 1 3770 Flute Drive 1 7601 Andromedia Lane 1 6770 1st St 1 5053 Tree Drive 1 3982 Weaver Lane 1 9153 3rd Hwy 1 8602 Washington Ridge 1 9603 Texas Lane 1 9422 Washington Ridge 1 3486 Flute Ave 1 6355 4th Hwy 1 6971 Best Ridge 1 4519 Embaracadero St 1 9728 Britain Hwy 1 3263 Pine Ridge 1 2430 MLK Ave 1 7041 Tree Ridge 1 2651 MLK Lane 1 2086 Francis Drive 1 4995 Weaver Ridge 1 4939 Oak Lane 1 5608 Solo St 1 1275 4th Ridge 1 2832 Andromedia Lane 1 4699 Texas Ridge 1 7628 4th Lane 1 6530 Weaver Ave 1 3659 Oak Lane 1 7488 Lincoln Lane 1 1218 Sky Hwy 1 2324 Texas Ridge 1 6536 MLK Hwy 1 3872 5th Drive 1 8618 Texas Lane 1 3753 Francis Lane 1 7551 Britain Lane 1 5499 Flute Ridge 1 4721 Cherokee Hwy 1 6985 Maple Lane 1 8021 Flute Ave 1 4574 Britain Hwy 1 9847 Elm St 1 8353 Britain Ridge 1 4053 Sky Lane 1 4653 Pine St 1 6550 Andromedia St 1 3929 Oak Drive 1 6719 Flute St 1 3127 Flute St 1 1213 4th Lane 1 7331 Sky Hwy 1 4983 MLK Ridge 1 6619 Flute Ave 1 5650 Rock Ave 1 7476 4th St 1 8957 Weaver Drive 1 8245 4th Hwy 1 4475 Lincoln Ridge 1 7121 Francis Lane 1 3029 5th Ave 1 2333 Maple Lane 1 5806 Embaracadero St 1 5765 Washington St 1 5071 Flute Ridge 1 7583 Washington Ave 1 5363 Weaver Lane 1 1512 Rock Lane 1 3814 Britain Drive 1 5812 3rd Hwy 1 8368 Cherokee Ave 1 4296 Pine Hwy 1 7575 Pine St 1 2337 Lincoln Hwy 1 9322 Rock Hwy 1 9007 Francis Hwy 1 8336 1st Ridge 1 2873 Flute Ave 1 9214 Texas Drive 1 4394 Oak St 1 8150 Washington Ridge 1 5622 Best Ridge 1 6045 Andromedia St 1 2905 Embaracadero Drive 1 7544 Washington Ave 1 7155 Apache Drive 1 6359 MLK Ridge 1 4965 MLK Drive 1 1091 1st Drive 1 8381 Solo Hwy 1 4234 Cherokee Lane 1 7299 Apache St 1 7733 Britain Lane 1 8096 Apache Hwy 1 1705 Weaver St 1 7061 Cherokee Drive 1 8211 Sky Hwy 1 2753 Cherokee Ave 1 5333 MLK Lane 1 1422 Flute Ave 1 8101 3rd Ridge 1 5821 2nd St 1 4905 Best Lane 1 4447 Francis Hwy 1 3706 4th Hwy 1 8809 Flute St 1 1840 Embaracadero Ave 1 3847 Elm Hwy 1 5621 4th Ave 1 3508 Washington St 1 4058 Tree Drive 1 2500 Tree St 1 3706 Texas Hwy 1 6608 MLK Hwy 1 5093 Flute Lane 1 3834 Pine St 1 6309 Cherokee Ave 1 2123 Texas Ave 1 8078 Britain Hwy 1 2492 Lincoln Lane 1 5168 5th Ave 1 2005 Texas Hwy 1 1818 Tree St 1 7108 Tree St 1 7797 Tree Ridge 1 6956 Maple Drive 1 9798 Sky Ridge 1 3707 Oak Ridge 1 7816 MLK Lane 1 9154 MLK Hwy 1 3052 Weaver Ridge 1 3751 Tree Hwy 1 5160 2nd Hwy 1 1320 Flute Lane 1 7615 Weaver Drive 1 6655 5th Drive 1 6888 Elm Ridge 1 7693 Britain Lane 1 2217 Tree Lane 1 8742 4th St 1 4239 Weaver Ave 1 8085 Andromedia St 1 2533 Elm St 1 1824 5th Lane 1 1809 Sky St 1 9748 Sky Drive 1 6660 MLK Drive 1 2850 Washington St 1 8493 Apache Drive 1 4486 Cherokee Ridge 1 2289 Weaver Ridge 1 7477 MLK Drive 1 3697 Apache Drive 1 7142 5th Lane 1 9682 Cherokee Ridge 1 6889 Cherokee St 1 9078 Francis Ridge 1 8917 Tree Ridge 1 2494 Andromedia Drive 1 5028 Maple Ridge 1 6035 Rock Ave 1 3246 Britain Ridge 1 9067 Texas Ave 1 4672 MLK St 1 7630 Rock Drive 1 4107 MLK Ridge 1 7644 Tree Ridge 1 3028 5th St 1 9633 MLK Lane 1 6931 Elm St 1 5475 Rock Lane 1 1454 5th Ridge 1 3915 Embaracadero St 1 3275 Pine St 1 4264 Lincoln Ridge 1 3422 Flute St 1 8212 Rock Ave 1 3447 Solo Ave 1 2215 Best Ave 1 9373 Pine Hwy 1 4268 2nd Ave 1 1956 Apache St 1 1628 Best Drive 1 2087 Apache Ave 1 8125 Texas Ridge 1 3320 5th Hwy 1 9724 Maple St 1 9484 Pine Drive 1 7976 Britain Drive 1 1507 Solo Ave 1 3952 Andromedia Lane 1 3841 Washington Lane 1 5236 Weaver Drive 1 9101 2nd Hwy 1 4872 Rock Ridge 1 1953 Sky Lane 1 3664 Francis Ridge 1 8864 Tree Ridge 1 2798 1st Ave 1 8822 Sky St 1 7701 Tree St 1 7785 Lincoln Lane 1 7552 3rd St 1 7327 Lincoln Drive 1 9352 Washington Ave 1 1469 Lincoln Drive 1 1910 Sky Ave 1 3769 Sky St 1 4866 4th Hwy 1 7684 Francis Ridge 1 8100 3rd Ave 1 8926 Texas Ridge 1 4633 5th Lane 1 3809 Texas Lane 1 9177 Texas Ave 1 8214 Flute St 1 8579 Apache Drive 1 3288 Tree Lane 1 2037 5th Drive 1 4780 Best Drive 1 1880 Weaver Drive 1 7835 Cherokee Hwy 1 6603 Francis Hwy 1 1102 Apache Hwy 1 5678 Lincoln Drive 1 7819 2nd Ave 1 9081 Cherokee Hwy 1 3167 2nd St 1 4693 Lincoln Hwy 1 6451 1st Hwy 1 1220 MLK Ave 1 7909 Andromedia Hwy 1 4652 Flute Drive 1 7504 Flute Drive 1 1364 Best St 1 1388 Embaracadero Hwy 1 5874 1st Hwy 1 5341 5th Ave 1 6137 MLK St 1 3289 Britain Drive 1 8782 3rd St 1 1830 Sky St 1 4994 Lincoln Drive 1 8492 Andromedia Ridge 1 9082 3rd Lane 1 5779 2nd Lane 1 1365 Francis Ave 1 4176 Britain Hwy 1 1087 Flute Drive 1 3930 Embaracadero St 1 4614 MLK Ave 1 5901 Elm Drive 1 4755 Best Lane 1 1989 Solo Lane 1 2777 Solo Drive 1 9935 4th Drive 1 4567 Pine Ave 1 9034 Weaver Ridge 1 3100 Best St 1 3495 Britain Drive 1 6375 2nd Lane 1 8897 Sky St 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 3653 Elm Drive 1 1985 5th Ave 1 6408 Weaver Ridge 1 3172 Tree Ridge 1 6658 Weaver St 1 2986 MLK Drive 1 2100 Francis Drive 1 2878 Britain Hwy 1 3006 Lincoln Ridge 1 6582 Elm Lane 1 5925 Tree Hwy 1 2697 Oak Drive 1 8954 Apache Lane 1 4615 Embaracadero Ave 1 7825 1st Ridge 1 2290 4th Ave 1 3995 Lincoln Hwy 1 7098 Lincoln Hwy 1 3492 Britain St 1 3592 MLK Ridge 1 3903 Oak Ave 1 5783 Oak Ave 1 6179 3rd Ridge 1 7314 Tree Drive 1 2808 Elm St 1 4020 Best Drive 1 9821 Francis Ave 1 3104 Sky Drive 1 7112 Weaver Ave 1 7740 MLK St 1 1532 Washington St 1 9751 Tree St 1 4554 Sky Ave 1 6574 4th Drive 1 6191 Oak Lane 1 5318 5th Ave 1 5445 Tree Hwy 1 9719 4th Lane 1 9245 Weaver Ridge 1 8198 Embaracadero Lane 1 9279 Oak Hwy 1 2280 4th Ave 1 1353 Washington St 1 1553 Lincoln St 1 8459 Apache Ave 1 5276 2nd Lane 1 5602 Britain St 1 3618 Sky Ave 1 9038 2nd Lane 1 2849 Pine Drive 1 5855 Apache St 1 8538 Texas Lane 1 2003 Maple Hwy 1 8081 Flute Ridge 1 2980 Sky Ridge 1 5865 Sky Lane 1 1358 Maple St 1 6939 3rd Hwy 1 6677 Andromedia Drive 1 9573 Weaver Ave 1 3658 Rock Drive 1 8524 Pine Lane 1 7717 Britain Hwy 1 3926 Rock Lane 1 1126 Texas Hwy 1 6678 Weaver Drive 1 2457 Washington Ave 1 6117 4th Ave 1 4055 2nd Drive 1 9988 Rock Ridge 1 1123 5th Lane 1 8306 1st Drive 1 9875 MLK Ave 1 7197 2nd Drive 1 7168 Andromedia Ridge 1 9730 2nd Hwy 1 8617 Best Ave 1 9980 Lincoln Ave 1 8876 1st St 1 2014 Rock Ave 1 1012 5th Lane 1 1833 Solo Ave 1 7649 Texas St 1 5667 4th Drive 1 1546 Cherokee Ave 1 4972 Francis Lane 1 4627 Elm Ridge 1 6738 Francis Hwy 1 3540 Maple St 1 9610 Cherokee St 1 4558 3rd Hwy 1 2644 MLK Drive 1 3419 Apache St 1 6331 MLK Ave 1 8946 2nd Drive 1 6384 5th Ridge 1 1643 Washington Hwy 1 2968 Andromedia Ave 1 4862 Lincoln Hwy 1 3900 Texas St 1 7897 Lincoln St 1 7529 Solo Ridge 1 3998 4th Hwy 1 5383 Maple Drive 1 4477 5th Ave 1 2603 Andromedia Hwy 1 1815 Cherokee Drive 1 1589 Pine St 1 8006 Maple Hwy 1 3110 Lincoln Lane 1 7846 Andromedia Drive 1 1845 Best St 1 4116 Embaracadero Lane 1 2577 Washington Drive 1 6838 Flute Lane 1 7135 Flute Lane 1 8991 Embaracadero Ave 1 4546 Tree St 1 6357 Texas Lane 1 1331 Britain Hwy 1 9911 Britain Lane 1 8758 5th St 1 5650 Sky Drive 1 2123 MLK Ridge 1 4981 Weaver St 1 5894 Flute Drive 1 7523 Oak Lane 1 1617 Rock Drive 1 2063 Weaver St 1 7295 Tree Hwy 1 6608 Apache Lane 1 8489 Pine Hwy 1 4095 MLK St 1 3097 4th Drive 1 5780 4th Ave 1 8456 1st Ave 1 7709 Rock Lane 1 9818 Cherokee Ave 1 8064 4th Ave 1 7405 Oak St 1 8542 Lincoln Ridge 1 2711 Britain Ave 1 9148 4th Hwy 1 6315 2nd Lane 1 7609 Rock St 1 1707 Sky Ave 1 6399 Oak Drive 1 1316 Britain Ridge 1 3094 Best Lane 1 6522 Apache Drive 1 5846 Weaver Drive 1 2526 Embaracadero Ave 1 6706 Francis Drive 1 4143 Maple Ridge 1 1957 Washington Ave 1 7954 Tree Ridge 1 3221 Solo Ridge 1 1135 Solo Lane 1 1992 Britain Drive 1 2230 1st St 1 9489 3rd St 1 4545 4th Ridge 1 3089 Oak Ridge 1 2299 Britain Drive 1 7178 Best Drive 1 5224 5th Lane 1 9633 4th St 1 3726 MLK Hwy 1 6409 Cherokee Drive 1 9720 Lincoln Hwy 1 1472 4th Drive 1 2757 4th Hwy 1 7782 Rock St 1 7791 Britain Ridge 1 6738 Washington Hwy 1 7397 4th Drive 1 3376 5th Drive 1 6493 Lincoln Lane 1 7705 Lincoln Drive 1 8118 Elm Ridge 1 5640 Embaracadero Lane 1 3835 5th Ave 1 8167 Apache Ave 1 5997 Embaracadero Drive 1 4214 MLK Ridge 1 9878 Washington Ave 1 6011 Britain St 1 4782 Sky Lane 1 2376 Sky Ridge 1 5074 3rd St 1 1267 Francis Hwy 1 5985 Lincoln Lane 1 6724 Andromedia St 1 7900 Sky Hwy 1 7236 Apache Lane 1 9942 Tree Ave 1 1328 Texas Lane 1 5226 Maple St 1 1725 Solo Lane 1 3929 Elm Ave 1 5924 Maple Drive 1 3418 Texas Lane 1 7393 Washington St 1 9685 Sky Ridge 1 1562 Britain St 1 6604 Apache Drive 1 4119 Texas St 1 1386 Britain St 1 7721 Washington Ridge 1 7930 Texas Ave 1 7002 Oak Hwy 1 3167 4th Ridge 1 5474 Weaver Hwy 1 5812 Oak St 1 1331 Elm Ridge 1 3808 5th Ave 1 6676 Tree Lane 1 7756 Pine Hwy 1 6128 Elm Lane 1 6256 Elm St 1 6684 Solo Lane 1 3656 Solo Ave 1 7574 4th St 1 8749 Tree St 1 6250 1st Ridge 1 8920 Best Ave 1 4955 Lincoln Ridge 1 8668 Flute St 1 8014 Embaracadero Drive 1 7819 Oak St 1 6501 5th Drive 1 3327 Lincoln Drive 1 7495 Washington Ave 1 2756 Britain Hwy 1 5769 Texas Lane 1 6859 Flute Ridge 1 5532 Weaver Ridge 1 9240 Britain Ave 1 6428 Andromedia Lane 1 9523 Solo Hwy 1 1376 Pine St 1 5771 Best St 1 7629 5th St 1 2253 Maple Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 4539 Texas St 1 4826 5th St 1 2311 4th St 1 2469 Francis Lane 1 3966 Oak Hwy 1 6874 Maple Ridge 1 5277 Texas Lane 1 4577 Sky Hwy 1 8233 Tree Drive 1 4291 Sky Hwy 1 1916 Elm St 1 1491 Francis Ridge 1 6329 Apache Ave 1 9760 Solo Lane 1 6853 Sky Hwy 1 1923 2nd Hwy 1 4335 1st St 1 8834 Elm Drive 1 4098 Weaver Ridge 1 2900 Sky Drive 1 1269 Flute Drive 1 5778 Pine Ridge 1 9224 Sky Drive 1 5499 Elm Hwy 1 4931 Maple Drive 1 2774 Apache Drive 1 9562 4th Ridge 1 1298 Maple Hwy 1 7877 3rd Ridge 1 6303 1st Drive 1 5051 Elm St 1 6934 Lincoln Ave 1 2397 Cherokee Ave 1 7281 Maple Hwy 1 1229 5th Ave 1 6494 4th Ave 1 7745 Washington Ridge 1 6981 Weaver St 1 1325 1st Lane 1 5862 Apache Ridge 1 8212 Flute Ridge 1 1654 Pine St 1 3743 Andromedia Ridge 1 6390 Apache St 1 8991 Texas Hwy 1 3771 4th St 1 3066 Francis Ave 1 4702 Texas Drive 1 7238 2nd St 1 5280 Pine Ave 1 9611 Pine Ridge 1 6206 3rd Ridge 1 9397 Francis St 1 8442 Britain Hwy 1 4981 Flute Hwy 1 4453 Best Ave 1 8080 Oak Lane 1 9214 Elm Ridge 1 4434 Lincoln Ave 1 6723 Best Drive 1 2117 Lincoln Hwy 1 8983 Tree St 1 4347 2nd Ridge 1 9418 5th Hwy 1 8655 Cherokee Lane 1 4937 Flute Drive 1 7240 5th Ridge 1 5585 Washington Drive 1 5743 4th Ridge 1 1371 Texas Lane 1 9529 4th Drive 1 9316 Pine Ave 1 1741 Best Ridge 1 8586 1st Ridge 1 2537 5th Ave 1 5914 Oak Ave 1 8624 Francis Ave 1 5771 Sky Ave 1 5868 Best Drive 1 8203 Lincoln Ave 1 7773 Tree Hwy 1 1173 Andromedia Ave 1 6484 Tree Drive 1 6851 3rd Drive 1 6741 Oak Ridge 1 1589 Best Ave 1 9397 5th Hwy 1 7316 Texas Ave 1 7201 Washington Ave 1 1381 Francis Ave 1 8718 Apache Lane 1 1416 Cherokee Ridge 1 1580 Maple Lane 1 8269 Sky Hwy 1 3323 1st Lane 1 9751 Sky Ridge 1 3092 Texas Drive 1 4814 Lincoln Lane 1 1681 Cherokee Hwy 1 8845 5th Ave 1 8821 Elm St 1 2725 Britain Ridge 1 9588 Solo St 1 6618 Cherokee Drive 1 7511 1st Ave 1 6583 MLK Ridge 1 4460 4th Lane 1 7571 Elm Ridge 1 3693 Pine Ave 1 3660 Andromedia Hwy 1 6605 Tree Ave 1 6067 Weaver Ridge 1 3227 Maple Ave 1 8667 Weaver Lane 1 4671 5th Ridge 1 9488 Best Drive 1 6378 Britain Ave 1 5782 Rock Drive 1 6092 5th Ave 1 9293 Pine Lane 1 4175 Elm Ridge 1 7144 Andromedia St 1 7121 Rock St 1 6945 Texas Hwy 1 1578 5th Lane 1 8049 4th St 1 2804 Best St 1 4496 Pine Lane 1 6681 Texas Ridge 1 5249 4th Ave 1 1620 Oak Ave 1 4625 MLK Drive 1 6435 Texas Ave 1 9760 4th Hwy 1 3966 Francis Ridge 1 4985 Sky Lane 1 2275 Best Lane 1 7500 Texas Ridge 1 1929 Britain Drive 1 5663 Oak Lane 1 8770 1st Lane 1 2886 Tree Ridge 1 3041 3rd Ave 1 6467 Best Ave 1 8811 Maple Hwy 1 6193 1st Hwy 1 9929 Rock Drive 1 2903 Weaver Drive 1 8907 Tree Ave 1 2820 Britain St 1 9580 MLK Ave 1 2201 4th Lane 1 9856 Apache St 1 6165 Rock Ridge 1 5260 Francis Drive 1 5480 3rd Ridge 1 4642 Rock Ridge 1 4188 Britain Ave 1 5838 Pine Lane 1 8621 Best Ridge 1 8906 Elm Lane 1 4585 Francis Ave 1 5279 Pine Ridge 1 5839 Weaver Lane 1 5586 2nd St 1 4254 Best Ridge 1 3053 Lincoln Drive 1 3397 5th Ave 1 1951 Best Ave 1 7535 5th Lane 1 8917 Cherokee Lane 1 1536 Flute Drive 1 1028 Sky Lane 1 2668 Cherokee St 1 6717 Best Drive 1 1806 Weaver Ridge 1 8477 Francis Hwy 1 9369 Flute Hwy 1 2696 Cherokee Ridge 1 3184 Oak Ave 1 3282 4th Lane 1 4964 Elm Lane 1 8404 Embaracadero St 1 7783 Lincoln Hwy 1 3553 Texas Ave 1 7928 Maple Ridge 1 6259 Weaver St 1 7380 5th Hwy 1 8689 Maple Hwy 1 8704 Britain Lane 1 5455 Oak Hwy 1 3039 Oak Hwy 1 7066 Texas Ave 1 5211 Weaver Drive 1 5540 Sky St 1 4910 1st Lane 1 3171 Andromedia Lane 1 7162 Maple Ave 1 6429 4th Hwy 1 5191 4th St 1 7534 MLK Hwy 1 5969 Francis St 1 4793 4th Ridge 1 6751 5th Hwy 1 8548 Cherokee Ridge 1 4907 Andromedia Drive 1 8949 Rock Hwy 1 1540 Apache Lane 1 6492 4th Lane 1 2509 Rock Drive 1 2654 Embaracadero St 1 5124 Maple St 1 1687 3rd Lane 1 2306 5th Lane 1 4369 Maple Lane 1 1273 Rock Lane 1 6955 Pine Drive 1 8983 Francis Ridge 1 3177 MLK Ridge 1 5584 Britain Lane 1 9278 Francis Ridge 1 1810 Elm Hwy 1 3488 Flute Lane 1 4154 Lincoln Hwy 1 2646 MLK Drive 1 9325 Lincoln Drive 1 8204 Pine Lane 1 5269 Flute Hwy 1 2539 Embaracadero Ridge 1 9286 Oak Ave 1 7502 Rock Lane 1 4755 1st St 1 9573 2nd Ave 1 1240 Tree Lane 1 6259 Lincoln Hwy 1 2318 Washington Hwy 1 3416 Washington Drive 1 5994 5th Ave 1 4905 Francis Ave 1 5649 Texas Ave 1 8940 Elm Ave 1 5904 1st Drive 1 5352 Lincoln Drive 1 3590 Best Hwy 1 8941 Solo Ridge 1 7460 Apache Lane 1 9169 Pine Ridge 1 7281 Oak St 1 8701 5th Lane 1 9138 1st St 1 3550 Washington Ave 1 2003 2nd Hwy 1 4434 Weaver St 1 4279 Solo Drive 1 2644 Elm Drive 1 2862 Tree Ridge 1 9109 Britain Drive 1 6456 Andromedia Drive 1 9816 Britain St 1 9879 Apache Drive 1 5189 Francis Drive 1 2914 Oak Drive 1 4876 Washington Drive 1 7426 Rock Drive 1 8973 Washington St 1 1832 Elm Hwy 1 3525 3rd Hwy 1 2654 Elm Drive 1 1679 2nd Hwy 1 4158 Washington Lane 1 3878 Tree Lane 1 3998 Flute St 1 5431 3rd Ridge 1 2889 Francis St 1 3790 Andromedia Hwy 1 7459 Flute St 1 8832 Pine Drive 1 5483 Francis Drive 1 5532 Francis Lane 1 9639 Britain Ridge 1 8995 1st Ave 1 7828 Cherokee Ave 1 2920 5th Ave 1 2100 MLK St 1 8509 Apache St 1 4629 Elm Ridge 1 3457 Texas Lane 1 1941 5th Ridge 1 8862 Maple Ridge 1 6149 Best Ridge 1 6317 Best St 1 4429 Washington St 1 4618 Flute Ave 1 2293 Washington Ave 1 2299 1st St 1 3797 Solo Lane 1 6848 Elm Hwy 1 1186 Rock St 1 5719 2nd Lane 1 1515 Pine Lane 1 7705 Best Ridge 1 2809 Francis Lane 1 8215 Flute Drive 1 3936 Tree Drive 1 1128 Maple Lane 1 Name: incident_location, dtype: int64 PROPERTY_DAMAGE : 3 YES 302 NO 338 Unknown 360 Name: property_damage, dtype: int64 AUTO_MODEL : 39 RSX 12 Accord 13 M5 15 X6 16 C300 18 3 Series 18 Corolla 20 ML350 20 Impreza 20 TL 20 CRV 20 Fusion 21 Highlander 22 Civic 22 Silverado 22 Ultima 23 X5 23 Escape 24 Maxima 24 Tahoe 24 Grand Cherokee 25 93 25 95 27 E400 27 F150 27 Camry 28 92x 28 Forrestor 28 Malibu 30 Pathfinder 31 Legacy 32 A5 32 Passat 33 Jetta 35 MDX 36 Neon 37 A3 37 Wrangler 42 RAM 43 Name: auto_model, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.property_damage.value_counts()
Unknown 360 NO 338 YES 302 Name: property_damage, dtype: int64
fraud_con['property_damage'] = fraud_con['property_damage'].astype('category')
fraud_con['property_damage'] = fraud_con['property_damage'].cat.codes
fraud_con.property_damage.value_counts()
1 360 0 338 2 302 Name: property_damage, dtype: int64
fraud_con.auto_model.value_counts()
RAM 43 Wrangler 42 A3 37 Neon 37 MDX 36 Jetta 35 Passat 33 A5 32 Legacy 32 Pathfinder 31 Malibu 30 Forrestor 28 92x 28 Camry 28 F150 27 E400 27 95 27 Grand Cherokee 25 93 25 Tahoe 24 Escape 24 Maxima 24 X5 23 Ultima 23 Silverado 22 Civic 22 Highlander 22 Fusion 21 ML350 20 Corolla 20 CRV 20 Impreza 20 TL 20 3 Series 18 C300 18 X6 16 M5 15 Accord 13 RSX 12 Name: auto_model, dtype: int64
fraud_con['auto_model'] = fraud_con['auto_model'].astype('category')
fraud_con['auto_model'] = fraud_con['auto_model'].cat.codes
fraud_con.auto_model.value_counts()
30 43 36 42 4 37 27 37 23 36 20 35 28 33 5 32 21 32 29 31 25 30 1 28 15 28 9 28 14 27 12 27 3 27 17 25 2 25 26 24 13 24 34 24 37 23 35 23 18 22 32 22 10 22 16 21 24 20 33 20 11 20 8 20 19 20 0 18 7 18 38 16 22 15 6 13 31 12 Name: auto_model, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 12-10-1995 1 11-09-2010 1 25-01-2008 1 28-10-1998 1 11-03-2014 1 25-12-2001 1 10-11-1992 1 28-01-1992 1 06-05-2002 1 07-02-2007 1 01-10-2010 1 10-02-1997 1 19-12-2004 1 29-07-1995 1 15-05-2000 1 04-03-2006 1 18-01-2014 1 21-05-2000 1 19-05-1990 1 24-09-1992 1 05-08-2014 1 22-02-1992 1 22-06-2009 1 02-12-2011 1 18-02-1995 1 28-01-1993 1 24-06-1998 1 27-05-1994 1 26-06-2009 1 01-03-1997 1 29-11-1999 1 05-12-2013 1 13-05-2001 1 16-09-2013 1 16-09-1990 1 25-05-2002 1 09-11-1992 1 13-07-2013 1 08-05-2005 1 26-08-2000 1 07-12-2001 1 22-03-1992 1 26-09-2010 1 17-05-1994 1 04-06-1996 1 22-10-2011 1 19-05-1992 1 09-04-2001 1 04-05-1995 1 22-07-2004 1 17-02-2009 1 08-04-1996 1 11-12-1992 1 21-06-1994 1 01-10-2001 1 29-09-2001 1 11-07-2007 1 29-02-2004 1 13-04-2006 1 01-10-2006 1 29-01-1993 1 28-05-2014 1 19-12-1997 1 04-02-1992 1 10-10-2005 1 21-09-1990 1 26-11-2001 1 28-10-1997 1 20-11-2005 1 05-07-2007 1 14-04-1993 1 03-01-2015 1 18-06-1997 1 28-04-2013 1 03-11-2009 1 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21-04-2006 1 07-08-1994 1 26-06-2001 1 28-07-2008 1 03-05-1991 1 12-12-1998 1 09-02-2000 1 08-02-1991 1 18-07-2002 1 16-11-1991 1 14-02-1992 1 19-09-2004 1 03-08-2009 1 06-02-2009 1 14-09-2001 1 17-06-2009 1 04-11-2010 1 13-11-1995 1 20-11-2010 1 30-12-2011 1 08-09-2009 1 23-07-1996 1 28-07-2014 1 17-02-2010 1 25-11-2002 1 08-06-2005 1 31-12-2003 1 18-02-1997 1 09-03-1999 1 11-10-2003 1 16-03-2003 1 23-08-1996 1 15-02-2001 1 18-12-1994 1 15-08-1990 1 21-03-2008 1 11-04-2005 1 09-02-1993 1 30-01-1992 1 15-11-2000 1 03-08-2010 1 11-01-2009 1 17-08-2011 1 07-11-2012 1 18-06-1993 1 04-11-2004 1 13-02-2003 1 14-01-2008 1 01-03-2012 1 23-07-1992 1 09-11-2008 1 17-06-2008 1 22-11-2012 1 05-07-1999 1 19-04-2007 1 31-12-2012 1 29-07-2012 1 11-07-1991 1 17-07-2005 1 25-12-1997 1 25-12-2013 2 20-09-1990 2 29-01-1998 2 05-07-2014 2 21-12-2002 2 25-09-2001 2 16-05-2008 2 11-11-1998 2 09-08-2004 2 11-03-2010 2 14-12-1991 2 21-09-1996 2 14-04-1992 2 28-12-1991 2 05-01-1992 2 27-07-2014 2 28-12-2002 2 25-05-1990 2 07-04-1999 2 09-07-2002 2 15-11-1997 2 03-02-1997 2 04-05-2000 2 20-07-1991 2 21-09-2005 2 08-11-2009 2 04-06-2000 2 24-06-1990 2 07-07-1996 2 28-01-2010 2 15-05-1997 2 22-08-1991 2 06-05-2007 2 07-12-1999 2 19-09-1995 2 30-08-1993 2 09-03-2003 2 16-07-2002 2 07-12-1995 2 14-07-1997 2 03-01-2004 2 07-11-1997 2 29-09-1999 2 28-04-1992 3 05-08-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INSURED_RELATIONSHIP : 6 unmarried 141 wife 155 husband 170 not-in-family 174 other-relative 177 own-child 183 Name: insured_relationship, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 19-02-2015 10 11-02-2015 10 07-02-2015 10 10-02-2015 10 25-01-2015 10 26-01-2015 11 29-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 03-02-2015 13 23-01-2015 13 09-02-2015 13 27-01-2015 13 22-01-2015 14 20-02-2015 14 27-02-2015 14 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-01-2015 15 05-02-2015 16 16-02-2015 16 15-02-2015 16 16-01-2015 16 13-02-2015 16 09-01-2015 17 26-02-2015 17 08-02-2015 17 24-02-2015 17 06-01-2015 17 03-01-2015 18 18-01-2015 18 01-02-2015 18 28-02-2015 18 14-02-2015 18 25-02-2015 18 20-01-2015 18 21-02-2015 19 23-02-2015 19 01-01-2015 19 14-01-2015 19 12-01-2015 19 21-01-2015 19 22-02-2015 20 06-02-2015 20 12-02-2015 20 31-01-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 24-01-2015 24 10-01-2015 24 04-02-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_TYPE : 4 Parked Car 84 Vehicle Theft 94 Single Vehicle Collision 403 Multi-vehicle Collision 419 Name: incident_type, dtype: int64 COLLISION_TYPE : 4 Unknown 178 Front Collision 254 Side Collision 276 Rear Collision 292 Name: collision_type, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 9043 Maple Hwy 1 3414 Elm Ave 1 6581 Rock Ridge 1 3654 Cherokee Ave 1 3340 3rd Hwy 1 6668 Andromedia Ridge 1 8638 3rd Ave 1 5104 Francis Drive 1 9787 Andromedia Ave 1 5639 1st Ridge 1 9706 MLK Lane 1 2352 MLK Drive 1 9360 3rd Drive 1 3439 Andromedia Hwy 1 9105 Tree Lane 1 2048 3rd Ridge 1 5061 Francis Ave 1 8281 Lincoln Lane 1 6044 Weaver Drive 1 4710 Lincoln Hwy 1 1879 4th Lane 1 9734 2nd Ridge 1 1133 Apache St 1 7447 Lincoln Ridge 1 9918 Andromedia Drive 1 1306 Andromedia St 1 3777 Maple Ave 1 8453 Elm St 1 1215 Pine Hwy 1 5455 Tree Ridge 1 2733 Texas Drive 1 6104 Oak Ave 1 3799 Embaracadero Drive 1 4237 4th St 1 6834 1st Drive 1 7466 MLK Ridge 1 3423 Francis Ave 1 1110 4th Drive 1 1738 Solo Lane 1 3098 Oak Lane 1 4492 Andromedia Ave 1 3842 Solo Ridge 1 2942 1st Lane 1 7756 Solo Drive 1 8872 Oak Ridge 1 4939 Best St 1 2220 1st Lane 1 2787 MLK St 1 3796 Cherokee Drive 1 4835 Britain Ridge 1 9657 5th Ave 1 5971 5th Hwy 1 4232 Britain Ridge 1 6260 5th Lane 1 8805 Cherokee Drive 1 3805 Lincoln Hwy 1 1558 1st Ridge 1 3925 Sky St 1 6751 Pine Ridge 1 5418 Britain Ave 1 9794 Embaracadero St 1 7434 Oak Hwy 1 6516 Solo Drive 1 5459 MLK Ave 1 2889 Weaver St 1 3618 Maple Lane 1 4272 Oak Ridge 1 2272 Embaracadero Drive 1 7223 Embaracadero St 1 3102 Apache St 1 1919 4th Lane 1 1618 Maple Hwy 1 3196 Cherokee St 1 3061 Francis Hwy 1 7428 Sky Hwy 1 6110 Rock Ridge 1 2823 Weaver Lane 1 2950 MLK Ave 1 3492 Flute Lane 1 9633 Rock Hwy 1 4231 3rd Ave 1 2193 4th Ridge 1 7069 4th Hwy 1 4885 Oak Lane 1 7693 Cherokee Lane 1 8639 5th Hwy 1 4242 Rock Lane 1 9317 Apache Ave 1 1515 Embaracadero St 1 9103 MLK Lane 1 2352 Sky Drive 1 6443 Washington Ridge 1 5380 Pine St 1 6479 Francis Ave 1 8879 1st Drive 1 3087 Oak Hwy 1 2199 Texas Drive 1 2204 Washington Lane 1 1437 3rd Lane 1 9744 Texas Drive 1 2865 Maple Lane 1 1699 Oak Drive 1 9236 2nd Hwy 1 4538 Flute Hwy 1 7780 Flute Lane 1 1529 Elm Ridge 1 2617 Andromedia Drive 1 7923 Elm Ave 1 9070 Tree Ave 1 7121 Britain Drive 1 6068 2nd St 1 5201 Texas Hwy 1 1914 Francis St 1 9169 Cherokee Hwy 1 5506 Best St 1 1248 MLK Ridge 1 6731 Andromedia Hwy 1 9020 Elm Ave 1 6058 Andromedia Hwy 1 8492 Weaver Hwy 1 3567 4th Drive 1 1030 Pine Lane 1 3311 2nd Drive 1 4390 4th Drive 1 7877 Sky Lane 1 9742 5th Ridge 1 8188 Tree Ave 1 1655 Francis Hwy 1 1555 Washington Lane 1 4431 Rock St 1 6634 Texas Ridge 1 3982 Washington Hwy 1 7582 Pine Drive 1 5812 Weaver Ave 1 5058 4th Lane 1 5790 Flute Ridge 1 7082 Oak Ridge 1 6440 Rock Lane 1 5630 1st Drive 1 9737 Solo Hwy 1 1598 3rd Drive 1 9910 Maple Ave 1 7042 Maple Ridge 1 5312 Francis Ridge 1 3220 Rock Drive 1 2381 1st Hwy 1 7973 4th St 1 6158 Sky Ridge 1 9138 3rd St 1 9239 Washington Ridge 1 5868 Sky Hwy 1 5071 1st Lane 1 8097 Maple Lane 1 5178 Weaver Hwy 1 8576 Andromedia St 1 9066 Best Ridge 1 3884 Pine Lane 1 9439 MLK St 1 3122 Apache Drive 1 6515 Oak Lane 1 3846 4th Hwy 1 7576 Pine Ridge 1 2078 3rd Ave 1 4414 Solo Drive 1 3639 Flute Hwy 1 8766 Lincoln Lane 1 3443 Maple Ridge 1 7253 MLK St 1 8964 Francis St 1 4857 Weaver St 1 6702 Andromedia St 1 6309 5th Ave 1 2577 Texas Ridge 1 3555 Francis Ridge 1 6957 Weaver Drive 1 5007 Oak St 1 9358 Texas Ridge 1 1346 5th Lane 1 7859 4th Ridge 1 1762 Maple Hwy 1 5022 1st St 1 6638 Tree Drive 1 3818 Texas Ridge 1 4687 5th Drive 1 7570 Cherokee Drive 1 9417 Tree Hwy 1 3770 Flute Drive 1 7601 Andromedia Lane 1 6770 1st St 1 5053 Tree Drive 1 3982 Weaver Lane 1 9153 3rd Hwy 1 8602 Washington Ridge 1 9603 Texas Lane 1 9422 Washington Ridge 1 3486 Flute Ave 1 6355 4th Hwy 1 6971 Best Ridge 1 4519 Embaracadero St 1 9728 Britain Hwy 1 3263 Pine Ridge 1 2430 MLK Ave 1 7041 Tree Ridge 1 2651 MLK Lane 1 2086 Francis Drive 1 4995 Weaver Ridge 1 4939 Oak Lane 1 5608 Solo St 1 1275 4th Ridge 1 2832 Andromedia Lane 1 4699 Texas Ridge 1 7628 4th Lane 1 6530 Weaver Ave 1 3659 Oak Lane 1 7488 Lincoln Lane 1 1218 Sky Hwy 1 2324 Texas Ridge 1 6536 MLK Hwy 1 3872 5th Drive 1 8618 Texas Lane 1 3753 Francis Lane 1 7551 Britain Lane 1 5499 Flute Ridge 1 4721 Cherokee Hwy 1 6985 Maple Lane 1 8021 Flute Ave 1 4574 Britain Hwy 1 9847 Elm St 1 8353 Britain Ridge 1 4053 Sky Lane 1 4653 Pine St 1 6550 Andromedia St 1 3929 Oak Drive 1 6719 Flute St 1 3127 Flute St 1 1213 4th Lane 1 7331 Sky Hwy 1 4983 MLK Ridge 1 6619 Flute Ave 1 5650 Rock Ave 1 7476 4th St 1 8957 Weaver Drive 1 8245 4th Hwy 1 4475 Lincoln Ridge 1 7121 Francis Lane 1 3029 5th Ave 1 2333 Maple Lane 1 5806 Embaracadero St 1 5765 Washington St 1 5071 Flute Ridge 1 7583 Washington Ave 1 5363 Weaver Lane 1 1512 Rock Lane 1 3814 Britain Drive 1 5812 3rd Hwy 1 8368 Cherokee Ave 1 4296 Pine Hwy 1 7575 Pine St 1 2337 Lincoln Hwy 1 9322 Rock Hwy 1 9007 Francis Hwy 1 8336 1st Ridge 1 2873 Flute Ave 1 9214 Texas Drive 1 4394 Oak St 1 8150 Washington Ridge 1 5622 Best Ridge 1 6045 Andromedia St 1 2905 Embaracadero Drive 1 7544 Washington Ave 1 7155 Apache Drive 1 6359 MLK Ridge 1 4965 MLK Drive 1 1091 1st Drive 1 8381 Solo Hwy 1 4234 Cherokee Lane 1 7299 Apache St 1 7733 Britain Lane 1 8096 Apache Hwy 1 1705 Weaver St 1 7061 Cherokee Drive 1 8211 Sky Hwy 1 2753 Cherokee Ave 1 5333 MLK Lane 1 1422 Flute Ave 1 8101 3rd Ridge 1 5821 2nd St 1 4905 Best Lane 1 4447 Francis Hwy 1 3706 4th Hwy 1 8809 Flute St 1 1840 Embaracadero Ave 1 3847 Elm Hwy 1 5621 4th Ave 1 3508 Washington St 1 4058 Tree Drive 1 2500 Tree St 1 3706 Texas Hwy 1 6608 MLK Hwy 1 5093 Flute Lane 1 3834 Pine St 1 6309 Cherokee Ave 1 2123 Texas Ave 1 8078 Britain Hwy 1 2492 Lincoln Lane 1 5168 5th Ave 1 2005 Texas Hwy 1 1818 Tree St 1 7108 Tree St 1 7797 Tree Ridge 1 6956 Maple Drive 1 9798 Sky Ridge 1 3707 Oak Ridge 1 7816 MLK Lane 1 9154 MLK Hwy 1 3052 Weaver Ridge 1 3751 Tree Hwy 1 5160 2nd Hwy 1 1320 Flute Lane 1 7615 Weaver Drive 1 6655 5th Drive 1 6888 Elm Ridge 1 7693 Britain Lane 1 2217 Tree Lane 1 8742 4th St 1 4239 Weaver Ave 1 8085 Andromedia St 1 2533 Elm St 1 1824 5th Lane 1 1809 Sky St 1 9748 Sky Drive 1 6660 MLK Drive 1 2850 Washington St 1 8493 Apache Drive 1 4486 Cherokee Ridge 1 2289 Weaver Ridge 1 7477 MLK Drive 1 3697 Apache Drive 1 7142 5th Lane 1 9682 Cherokee Ridge 1 6889 Cherokee St 1 9078 Francis Ridge 1 8917 Tree Ridge 1 2494 Andromedia Drive 1 5028 Maple Ridge 1 6035 Rock Ave 1 3246 Britain Ridge 1 9067 Texas Ave 1 4672 MLK St 1 7630 Rock Drive 1 4107 MLK Ridge 1 7644 Tree Ridge 1 3028 5th St 1 9633 MLK Lane 1 6931 Elm St 1 5475 Rock Lane 1 1454 5th Ridge 1 3915 Embaracadero St 1 3275 Pine St 1 4264 Lincoln Ridge 1 3422 Flute St 1 8212 Rock Ave 1 3447 Solo Ave 1 2215 Best Ave 1 9373 Pine Hwy 1 4268 2nd Ave 1 1956 Apache St 1 1628 Best Drive 1 2087 Apache Ave 1 8125 Texas Ridge 1 3320 5th Hwy 1 9724 Maple St 1 9484 Pine Drive 1 7976 Britain Drive 1 1507 Solo Ave 1 3952 Andromedia Lane 1 3841 Washington Lane 1 5236 Weaver Drive 1 9101 2nd Hwy 1 4872 Rock Ridge 1 1953 Sky Lane 1 3664 Francis Ridge 1 8864 Tree Ridge 1 2798 1st Ave 1 8822 Sky St 1 7701 Tree St 1 7785 Lincoln Lane 1 7552 3rd St 1 7327 Lincoln Drive 1 9352 Washington Ave 1 1469 Lincoln Drive 1 1910 Sky Ave 1 3769 Sky St 1 4866 4th Hwy 1 7684 Francis Ridge 1 8100 3rd Ave 1 8926 Texas Ridge 1 4633 5th Lane 1 3809 Texas Lane 1 9177 Texas Ave 1 8214 Flute St 1 8579 Apache Drive 1 3288 Tree Lane 1 2037 5th Drive 1 4780 Best Drive 1 1880 Weaver Drive 1 7835 Cherokee Hwy 1 6603 Francis Hwy 1 1102 Apache Hwy 1 5678 Lincoln Drive 1 7819 2nd Ave 1 9081 Cherokee Hwy 1 3167 2nd St 1 4693 Lincoln Hwy 1 6451 1st Hwy 1 1220 MLK Ave 1 7909 Andromedia Hwy 1 4652 Flute Drive 1 7504 Flute Drive 1 1364 Best St 1 1388 Embaracadero Hwy 1 5874 1st Hwy 1 5341 5th Ave 1 6137 MLK St 1 3289 Britain Drive 1 8782 3rd St 1 1830 Sky St 1 4994 Lincoln Drive 1 8492 Andromedia Ridge 1 9082 3rd Lane 1 5779 2nd Lane 1 1365 Francis Ave 1 4176 Britain Hwy 1 1087 Flute Drive 1 3930 Embaracadero St 1 4614 MLK Ave 1 5901 Elm Drive 1 4755 Best Lane 1 1989 Solo Lane 1 2777 Solo Drive 1 9935 4th Drive 1 4567 Pine Ave 1 9034 Weaver Ridge 1 3100 Best St 1 3495 Britain Drive 1 6375 2nd Lane 1 8897 Sky St 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 3653 Elm Drive 1 1985 5th Ave 1 6408 Weaver Ridge 1 3172 Tree Ridge 1 6658 Weaver St 1 2986 MLK Drive 1 2100 Francis Drive 1 2878 Britain Hwy 1 3006 Lincoln Ridge 1 6582 Elm Lane 1 5925 Tree Hwy 1 2697 Oak Drive 1 8954 Apache Lane 1 4615 Embaracadero Ave 1 7825 1st Ridge 1 2290 4th Ave 1 3995 Lincoln Hwy 1 7098 Lincoln Hwy 1 3492 Britain St 1 3592 MLK Ridge 1 3903 Oak Ave 1 5783 Oak Ave 1 6179 3rd Ridge 1 7314 Tree Drive 1 2808 Elm St 1 4020 Best Drive 1 9821 Francis Ave 1 3104 Sky Drive 1 7112 Weaver Ave 1 7740 MLK St 1 1532 Washington St 1 9751 Tree St 1 4554 Sky Ave 1 6574 4th Drive 1 6191 Oak Lane 1 5318 5th Ave 1 5445 Tree Hwy 1 9719 4th Lane 1 9245 Weaver Ridge 1 8198 Embaracadero Lane 1 9279 Oak Hwy 1 2280 4th Ave 1 1353 Washington St 1 1553 Lincoln St 1 8459 Apache Ave 1 5276 2nd Lane 1 5602 Britain St 1 3618 Sky Ave 1 9038 2nd Lane 1 2849 Pine Drive 1 5855 Apache St 1 8538 Texas Lane 1 2003 Maple Hwy 1 8081 Flute Ridge 1 2980 Sky Ridge 1 5865 Sky Lane 1 1358 Maple St 1 6939 3rd Hwy 1 6677 Andromedia Drive 1 9573 Weaver Ave 1 3658 Rock Drive 1 8524 Pine Lane 1 7717 Britain Hwy 1 3926 Rock Lane 1 1126 Texas Hwy 1 6678 Weaver Drive 1 2457 Washington Ave 1 6117 4th Ave 1 4055 2nd Drive 1 9988 Rock Ridge 1 1123 5th Lane 1 8306 1st Drive 1 9875 MLK Ave 1 7197 2nd Drive 1 7168 Andromedia Ridge 1 9730 2nd Hwy 1 8617 Best Ave 1 9980 Lincoln Ave 1 8876 1st St 1 2014 Rock Ave 1 1012 5th Lane 1 1833 Solo Ave 1 7649 Texas St 1 5667 4th Drive 1 1546 Cherokee Ave 1 4972 Francis Lane 1 4627 Elm Ridge 1 6738 Francis Hwy 1 3540 Maple St 1 9610 Cherokee St 1 4558 3rd Hwy 1 2644 MLK Drive 1 3419 Apache St 1 6331 MLK Ave 1 8946 2nd Drive 1 6384 5th Ridge 1 1643 Washington Hwy 1 2968 Andromedia Ave 1 4862 Lincoln Hwy 1 3900 Texas St 1 7897 Lincoln St 1 7529 Solo Ridge 1 3998 4th Hwy 1 5383 Maple Drive 1 4477 5th Ave 1 2603 Andromedia Hwy 1 1815 Cherokee Drive 1 1589 Pine St 1 8006 Maple Hwy 1 3110 Lincoln Lane 1 7846 Andromedia Drive 1 1845 Best St 1 4116 Embaracadero Lane 1 2577 Washington Drive 1 6838 Flute Lane 1 7135 Flute Lane 1 8991 Embaracadero Ave 1 4546 Tree St 1 6357 Texas Lane 1 1331 Britain Hwy 1 9911 Britain Lane 1 8758 5th St 1 5650 Sky Drive 1 2123 MLK Ridge 1 4981 Weaver St 1 5894 Flute Drive 1 7523 Oak Lane 1 1617 Rock Drive 1 2063 Weaver St 1 7295 Tree Hwy 1 6608 Apache Lane 1 8489 Pine Hwy 1 4095 MLK St 1 3097 4th Drive 1 5780 4th Ave 1 8456 1st Ave 1 7709 Rock Lane 1 9818 Cherokee Ave 1 8064 4th Ave 1 7405 Oak St 1 8542 Lincoln Ridge 1 2711 Britain Ave 1 9148 4th Hwy 1 6315 2nd Lane 1 7609 Rock St 1 1707 Sky Ave 1 6399 Oak Drive 1 1316 Britain Ridge 1 3094 Best Lane 1 6522 Apache Drive 1 5846 Weaver Drive 1 2526 Embaracadero Ave 1 6706 Francis Drive 1 4143 Maple Ridge 1 1957 Washington Ave 1 7954 Tree Ridge 1 3221 Solo Ridge 1 1135 Solo Lane 1 1992 Britain Drive 1 2230 1st St 1 9489 3rd St 1 4545 4th Ridge 1 3089 Oak Ridge 1 2299 Britain Drive 1 7178 Best Drive 1 5224 5th Lane 1 9633 4th St 1 3726 MLK Hwy 1 6409 Cherokee Drive 1 9720 Lincoln Hwy 1 1472 4th Drive 1 2757 4th Hwy 1 7782 Rock St 1 7791 Britain Ridge 1 6738 Washington Hwy 1 7397 4th Drive 1 3376 5th Drive 1 6493 Lincoln Lane 1 7705 Lincoln Drive 1 8118 Elm Ridge 1 5640 Embaracadero Lane 1 3835 5th Ave 1 8167 Apache Ave 1 5997 Embaracadero Drive 1 4214 MLK Ridge 1 9878 Washington Ave 1 6011 Britain St 1 4782 Sky Lane 1 2376 Sky Ridge 1 5074 3rd St 1 1267 Francis Hwy 1 5985 Lincoln Lane 1 6724 Andromedia St 1 7900 Sky Hwy 1 7236 Apache Lane 1 9942 Tree Ave 1 1328 Texas Lane 1 5226 Maple St 1 1725 Solo Lane 1 3929 Elm Ave 1 5924 Maple Drive 1 3418 Texas Lane 1 7393 Washington St 1 9685 Sky Ridge 1 1562 Britain St 1 6604 Apache Drive 1 4119 Texas St 1 1386 Britain St 1 7721 Washington Ridge 1 7930 Texas Ave 1 7002 Oak Hwy 1 3167 4th Ridge 1 5474 Weaver Hwy 1 5812 Oak St 1 1331 Elm Ridge 1 3808 5th Ave 1 6676 Tree Lane 1 7756 Pine Hwy 1 6128 Elm Lane 1 6256 Elm St 1 6684 Solo Lane 1 3656 Solo Ave 1 7574 4th St 1 8749 Tree St 1 6250 1st Ridge 1 8920 Best Ave 1 4955 Lincoln Ridge 1 8668 Flute St 1 8014 Embaracadero Drive 1 7819 Oak St 1 6501 5th Drive 1 3327 Lincoln Drive 1 7495 Washington Ave 1 2756 Britain Hwy 1 5769 Texas Lane 1 6859 Flute Ridge 1 5532 Weaver Ridge 1 9240 Britain Ave 1 6428 Andromedia Lane 1 9523 Solo Hwy 1 1376 Pine St 1 5771 Best St 1 7629 5th St 1 2253 Maple Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 4539 Texas St 1 4826 5th St 1 2311 4th St 1 2469 Francis Lane 1 3966 Oak Hwy 1 6874 Maple Ridge 1 5277 Texas Lane 1 4577 Sky Hwy 1 8233 Tree Drive 1 4291 Sky Hwy 1 1916 Elm St 1 1491 Francis Ridge 1 6329 Apache Ave 1 9760 Solo Lane 1 6853 Sky Hwy 1 1923 2nd Hwy 1 4335 1st St 1 8834 Elm Drive 1 4098 Weaver Ridge 1 2900 Sky Drive 1 1269 Flute Drive 1 5778 Pine Ridge 1 9224 Sky Drive 1 5499 Elm Hwy 1 4931 Maple Drive 1 2774 Apache Drive 1 9562 4th Ridge 1 1298 Maple Hwy 1 7877 3rd Ridge 1 6303 1st Drive 1 5051 Elm St 1 6934 Lincoln Ave 1 2397 Cherokee Ave 1 7281 Maple Hwy 1 1229 5th Ave 1 6494 4th Ave 1 7745 Washington Ridge 1 6981 Weaver St 1 1325 1st Lane 1 5862 Apache Ridge 1 8212 Flute Ridge 1 1654 Pine St 1 3743 Andromedia Ridge 1 6390 Apache St 1 8991 Texas Hwy 1 3771 4th St 1 3066 Francis Ave 1 4702 Texas Drive 1 7238 2nd St 1 5280 Pine Ave 1 9611 Pine Ridge 1 6206 3rd Ridge 1 9397 Francis St 1 8442 Britain Hwy 1 4981 Flute Hwy 1 4453 Best Ave 1 8080 Oak Lane 1 9214 Elm Ridge 1 4434 Lincoln Ave 1 6723 Best Drive 1 2117 Lincoln Hwy 1 8983 Tree St 1 4347 2nd Ridge 1 9418 5th Hwy 1 8655 Cherokee Lane 1 4937 Flute Drive 1 7240 5th Ridge 1 5585 Washington Drive 1 5743 4th Ridge 1 1371 Texas Lane 1 9529 4th Drive 1 9316 Pine Ave 1 1741 Best Ridge 1 8586 1st Ridge 1 2537 5th Ave 1 5914 Oak Ave 1 8624 Francis Ave 1 5771 Sky Ave 1 5868 Best Drive 1 8203 Lincoln Ave 1 7773 Tree Hwy 1 1173 Andromedia Ave 1 6484 Tree Drive 1 6851 3rd Drive 1 6741 Oak Ridge 1 1589 Best Ave 1 9397 5th Hwy 1 7316 Texas Ave 1 7201 Washington Ave 1 1381 Francis Ave 1 8718 Apache Lane 1 1416 Cherokee Ridge 1 1580 Maple Lane 1 8269 Sky Hwy 1 3323 1st Lane 1 9751 Sky Ridge 1 3092 Texas Drive 1 4814 Lincoln Lane 1 1681 Cherokee Hwy 1 8845 5th Ave 1 8821 Elm St 1 2725 Britain Ridge 1 9588 Solo St 1 6618 Cherokee Drive 1 7511 1st Ave 1 6583 MLK Ridge 1 4460 4th Lane 1 7571 Elm Ridge 1 3693 Pine Ave 1 3660 Andromedia Hwy 1 6605 Tree Ave 1 6067 Weaver Ridge 1 3227 Maple Ave 1 8667 Weaver Lane 1 4671 5th Ridge 1 9488 Best Drive 1 6378 Britain Ave 1 5782 Rock Drive 1 6092 5th Ave 1 9293 Pine Lane 1 4175 Elm Ridge 1 7144 Andromedia St 1 7121 Rock St 1 6945 Texas Hwy 1 1578 5th Lane 1 8049 4th St 1 2804 Best St 1 4496 Pine Lane 1 6681 Texas Ridge 1 5249 4th Ave 1 1620 Oak Ave 1 4625 MLK Drive 1 6435 Texas Ave 1 9760 4th Hwy 1 3966 Francis Ridge 1 4985 Sky Lane 1 2275 Best Lane 1 7500 Texas Ridge 1 1929 Britain Drive 1 5663 Oak Lane 1 8770 1st Lane 1 2886 Tree Ridge 1 3041 3rd Ave 1 6467 Best Ave 1 8811 Maple Hwy 1 6193 1st Hwy 1 9929 Rock Drive 1 2903 Weaver Drive 1 8907 Tree Ave 1 2820 Britain St 1 9580 MLK Ave 1 2201 4th Lane 1 9856 Apache St 1 6165 Rock Ridge 1 5260 Francis Drive 1 5480 3rd Ridge 1 4642 Rock Ridge 1 4188 Britain Ave 1 5838 Pine Lane 1 8621 Best Ridge 1 8906 Elm Lane 1 4585 Francis Ave 1 5279 Pine Ridge 1 5839 Weaver Lane 1 5586 2nd St 1 4254 Best Ridge 1 3053 Lincoln Drive 1 3397 5th Ave 1 1951 Best Ave 1 7535 5th Lane 1 8917 Cherokee Lane 1 1536 Flute Drive 1 1028 Sky Lane 1 2668 Cherokee St 1 6717 Best Drive 1 1806 Weaver Ridge 1 8477 Francis Hwy 1 9369 Flute Hwy 1 2696 Cherokee Ridge 1 3184 Oak Ave 1 3282 4th Lane 1 4964 Elm Lane 1 8404 Embaracadero St 1 7783 Lincoln Hwy 1 3553 Texas Ave 1 7928 Maple Ridge 1 6259 Weaver St 1 7380 5th Hwy 1 8689 Maple Hwy 1 8704 Britain Lane 1 5455 Oak Hwy 1 3039 Oak Hwy 1 7066 Texas Ave 1 5211 Weaver Drive 1 5540 Sky St 1 4910 1st Lane 1 3171 Andromedia Lane 1 7162 Maple Ave 1 6429 4th Hwy 1 5191 4th St 1 7534 MLK Hwy 1 5969 Francis St 1 4793 4th Ridge 1 6751 5th Hwy 1 8548 Cherokee Ridge 1 4907 Andromedia Drive 1 8949 Rock Hwy 1 1540 Apache Lane 1 6492 4th Lane 1 2509 Rock Drive 1 2654 Embaracadero St 1 5124 Maple St 1 1687 3rd Lane 1 2306 5th Lane 1 4369 Maple Lane 1 1273 Rock Lane 1 6955 Pine Drive 1 8983 Francis Ridge 1 3177 MLK Ridge 1 5584 Britain Lane 1 9278 Francis Ridge 1 1810 Elm Hwy 1 3488 Flute Lane 1 4154 Lincoln Hwy 1 2646 MLK Drive 1 9325 Lincoln Drive 1 8204 Pine Lane 1 5269 Flute Hwy 1 2539 Embaracadero Ridge 1 9286 Oak Ave 1 7502 Rock Lane 1 4755 1st St 1 9573 2nd Ave 1 1240 Tree Lane 1 6259 Lincoln Hwy 1 2318 Washington Hwy 1 3416 Washington Drive 1 5994 5th Ave 1 4905 Francis Ave 1 5649 Texas Ave 1 8940 Elm Ave 1 5904 1st Drive 1 5352 Lincoln Drive 1 3590 Best Hwy 1 8941 Solo Ridge 1 7460 Apache Lane 1 9169 Pine Ridge 1 7281 Oak St 1 8701 5th Lane 1 9138 1st St 1 3550 Washington Ave 1 2003 2nd Hwy 1 4434 Weaver St 1 4279 Solo Drive 1 2644 Elm Drive 1 2862 Tree Ridge 1 9109 Britain Drive 1 6456 Andromedia Drive 1 9816 Britain St 1 9879 Apache Drive 1 5189 Francis Drive 1 2914 Oak Drive 1 4876 Washington Drive 1 7426 Rock Drive 1 8973 Washington St 1 1832 Elm Hwy 1 3525 3rd Hwy 1 2654 Elm Drive 1 1679 2nd Hwy 1 4158 Washington Lane 1 3878 Tree Lane 1 3998 Flute St 1 5431 3rd Ridge 1 2889 Francis St 1 3790 Andromedia Hwy 1 7459 Flute St 1 8832 Pine Drive 1 5483 Francis Drive 1 5532 Francis Lane 1 9639 Britain Ridge 1 8995 1st Ave 1 7828 Cherokee Ave 1 2920 5th Ave 1 2100 MLK St 1 8509 Apache St 1 4629 Elm Ridge 1 3457 Texas Lane 1 1941 5th Ridge 1 8862 Maple Ridge 1 6149 Best Ridge 1 6317 Best St 1 4429 Washington St 1 4618 Flute Ave 1 2293 Washington Ave 1 2299 1st St 1 3797 Solo Lane 1 6848 Elm Hwy 1 1186 Rock St 1 5719 2nd Lane 1 1515 Pine Lane 1 7705 Best Ridge 1 2809 Francis Lane 1 8215 Flute Drive 1 3936 Tree Drive 1 1128 Maple Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.insured_relationship.value_counts()
own-child 183 other-relative 177 not-in-family 174 husband 170 wife 155 unmarried 141 Name: insured_relationship, dtype: int64
fraud_con['insured_relationship'] = fraud_con['insured_relationship'].astype('category')
fraud_con['insured_relationship'] = fraud_con['insured_relationship'].cat.codes
fraud_con.insured_relationship.value_counts()
3 183 2 177 1 174 0 170 5 155 4 141 Name: insured_relationship, dtype: int64
fraud_con.collision_type.value_counts()
Rear Collision 292 Side Collision 276 Front Collision 254 Unknown 178 Name: collision_type, dtype: int64
fraud_con['collision_type'] = fraud_con['collision_type'].astype('category')
fraud_con['collision_type'] = fraud_con['collision_type'].cat.codes
fraud_con.collision_type.value_counts()
1 292 2 276 0 254 3 178 Name: collision_type, dtype: int64
fraud_con.incident_type.value_counts()
Multi-vehicle Collision 419 Single Vehicle Collision 403 Vehicle Theft 94 Parked Car 84 Name: incident_type, dtype: int64
fraud_con['incident_type'] = fraud_con['incident_type'].astype('category')
fraud_con['incident_type'] = fraud_con['incident_type'].cat.codes
fraud_con.incident_type.value_counts()
0 419 2 403 3 94 1 84 Name: incident_type, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 12-10-1995 1 11-09-2010 1 25-01-2008 1 28-10-1998 1 11-03-2014 1 25-12-2001 1 10-11-1992 1 28-01-1992 1 06-05-2002 1 07-02-2007 1 01-10-2010 1 10-02-1997 1 19-12-2004 1 29-07-1995 1 15-05-2000 1 04-03-2006 1 18-01-2014 1 21-05-2000 1 19-05-1990 1 24-09-1992 1 05-08-2014 1 22-02-1992 1 22-06-2009 1 02-12-2011 1 18-02-1995 1 28-01-1993 1 24-06-1998 1 27-05-1994 1 26-06-2009 1 01-03-1997 1 29-11-1999 1 05-12-2013 1 13-05-2001 1 16-09-2013 1 16-09-1990 1 25-05-2002 1 09-11-1992 1 13-07-2013 1 08-05-2005 1 26-08-2000 1 07-12-2001 1 22-03-1992 1 26-09-2010 1 17-05-1994 1 04-06-1996 1 22-10-2011 1 19-05-1992 1 09-04-2001 1 04-05-1995 1 22-07-2004 1 17-02-2009 1 08-04-1996 1 11-12-1992 1 21-06-1994 1 01-10-2001 1 29-09-2001 1 11-07-2007 1 29-02-2004 1 13-04-2006 1 01-10-2006 1 29-01-1993 1 28-05-2014 1 19-12-1997 1 04-02-1992 1 10-10-2005 1 21-09-1990 1 26-11-2001 1 28-10-1997 1 20-11-2005 1 05-07-2007 1 14-04-1993 1 03-01-2015 1 18-06-1997 1 28-04-2013 1 03-11-2009 1 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18-08-1996 1 04-07-2013 1 28-11-1991 1 03-03-1997 1 20-11-1997 1 13-10-1990 1 21-08-1991 1 05-09-1996 1 17-10-2014 1 18-06-1998 1 02-11-2012 1 25-01-2006 1 17-11-2009 1 18-11-2011 1 05-12-2014 1 07-12-1998 1 06-12-1995 1 11-02-1994 1 25-06-2011 1 31-07-2011 1 18-09-1999 1 02-06-2010 1 23-05-1999 1 18-12-2013 1 02-10-1992 1 13-11-2002 1 18-03-2008 1 11-01-2010 1 10-02-1994 1 07-04-2014 1 10-06-2001 1 26-01-2011 1 03-09-1991 1 12-02-2009 1 18-10-1996 1 11-11-1996 1 25-04-1991 1 04-06-2012 1 29-05-1995 1 30-04-2002 1 30-08-1997 1 25-06-2002 1 21-08-2010 1 15-01-1992 1 21-06-1997 1 15-07-2004 1 21-02-1999 1 03-02-2008 1 03-04-2001 1 28-08-1995 1 15-02-1990 1 12-08-1999 1 26-03-1990 1 06-01-2011 1 05-07-2001 1 20-07-1990 1 05-05-1998 1 22-01-1993 1 05-07-2003 1 25-04-2002 1 23-08-1994 1 28-03-2001 1 12-10-1994 1 28-04-1994 1 20-03-1992 1 18-01-1997 1 20-05-1995 1 07-01-2005 1 05-08-2012 1 11-12-1998 1 16-12-1998 1 01-01-2008 1 21-07-2008 1 30-06-2004 1 28-01-1998 1 22-02-2015 1 28-10-2006 1 21-04-2006 1 07-08-1994 1 26-06-2001 1 28-07-2008 1 03-05-1991 1 12-12-1998 1 09-02-2000 1 08-02-1991 1 18-07-2002 1 16-11-1991 1 14-02-1992 1 19-09-2004 1 03-08-2009 1 06-02-2009 1 14-09-2001 1 17-06-2009 1 04-11-2010 1 13-11-1995 1 20-11-2010 1 30-12-2011 1 08-09-2009 1 23-07-1996 1 28-07-2014 1 17-02-2010 1 25-11-2002 1 08-06-2005 1 31-12-2003 1 18-02-1997 1 09-03-1999 1 11-10-2003 1 16-03-2003 1 23-08-1996 1 15-02-2001 1 18-12-1994 1 15-08-1990 1 21-03-2008 1 11-04-2005 1 09-02-1993 1 30-01-1992 1 15-11-2000 1 03-08-2010 1 11-01-2009 1 17-08-2011 1 07-11-2012 1 18-06-1993 1 04-11-2004 1 13-02-2003 1 14-01-2008 1 01-03-2012 1 23-07-1992 1 09-11-2008 1 17-06-2008 1 22-11-2012 1 05-07-1999 1 19-04-2007 1 31-12-2012 1 29-07-2012 1 11-07-1991 1 17-07-2005 1 25-12-1997 1 25-12-2013 2 20-09-1990 2 29-01-1998 2 05-07-2014 2 21-12-2002 2 25-09-2001 2 16-05-2008 2 11-11-1998 2 09-08-2004 2 11-03-2010 2 14-12-1991 2 21-09-1996 2 14-04-1992 2 28-12-1991 2 05-01-1992 2 27-07-2014 2 28-12-2002 2 25-05-1990 2 07-04-1999 2 09-07-2002 2 15-11-1997 2 03-02-1997 2 04-05-2000 2 20-07-1991 2 21-09-2005 2 08-11-2009 2 04-06-2000 2 24-06-1990 2 07-07-1996 2 28-01-2010 2 15-05-1997 2 22-08-1991 2 06-05-2007 2 07-12-1999 2 19-09-1995 2 30-08-1993 2 09-03-2003 2 16-07-2002 2 07-12-1995 2 14-07-1997 2 03-01-2004 2 07-11-1997 2 29-09-1999 2 28-04-1992 3 05-08-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 19-02-2015 10 11-02-2015 10 07-02-2015 10 10-02-2015 10 25-01-2015 10 26-01-2015 11 29-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 03-02-2015 13 23-01-2015 13 09-02-2015 13 27-01-2015 13 22-01-2015 14 20-02-2015 14 27-02-2015 14 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-01-2015 15 05-02-2015 16 16-02-2015 16 15-02-2015 16 16-01-2015 16 13-02-2015 16 09-01-2015 17 26-02-2015 17 08-02-2015 17 24-02-2015 17 06-01-2015 17 03-01-2015 18 18-01-2015 18 01-02-2015 18 28-02-2015 18 14-02-2015 18 25-02-2015 18 20-01-2015 18 21-02-2015 19 23-02-2015 19 01-01-2015 19 14-01-2015 19 12-01-2015 19 21-01-2015 19 22-02-2015 20 06-02-2015 20 12-02-2015 20 31-01-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 24-01-2015 24 10-01-2015 24 04-02-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 9043 Maple Hwy 1 3414 Elm Ave 1 6581 Rock Ridge 1 3654 Cherokee Ave 1 3340 3rd Hwy 1 6668 Andromedia Ridge 1 8638 3rd Ave 1 5104 Francis Drive 1 9787 Andromedia Ave 1 5639 1st Ridge 1 9706 MLK Lane 1 2352 MLK Drive 1 9360 3rd Drive 1 3439 Andromedia Hwy 1 9105 Tree Lane 1 2048 3rd Ridge 1 5061 Francis Ave 1 8281 Lincoln Lane 1 6044 Weaver Drive 1 4710 Lincoln Hwy 1 1879 4th Lane 1 9734 2nd Ridge 1 1133 Apache St 1 7447 Lincoln Ridge 1 9918 Andromedia Drive 1 1306 Andromedia St 1 3777 Maple Ave 1 8453 Elm St 1 1215 Pine Hwy 1 5455 Tree Ridge 1 2733 Texas Drive 1 6104 Oak Ave 1 3799 Embaracadero Drive 1 4237 4th St 1 6834 1st Drive 1 7466 MLK Ridge 1 3423 Francis Ave 1 1110 4th Drive 1 1738 Solo Lane 1 3098 Oak Lane 1 4492 Andromedia Ave 1 3842 Solo Ridge 1 2942 1st Lane 1 7756 Solo Drive 1 8872 Oak Ridge 1 4939 Best St 1 2220 1st Lane 1 2787 MLK St 1 3796 Cherokee Drive 1 4835 Britain Ridge 1 9657 5th Ave 1 5971 5th Hwy 1 4232 Britain Ridge 1 6260 5th Lane 1 8805 Cherokee Drive 1 3805 Lincoln Hwy 1 1558 1st Ridge 1 3925 Sky St 1 6751 Pine Ridge 1 5418 Britain Ave 1 9794 Embaracadero St 1 7434 Oak Hwy 1 6516 Solo Drive 1 5459 MLK Ave 1 2889 Weaver St 1 3618 Maple Lane 1 4272 Oak Ridge 1 2272 Embaracadero Drive 1 7223 Embaracadero St 1 3102 Apache St 1 1919 4th Lane 1 1618 Maple Hwy 1 3196 Cherokee St 1 3061 Francis Hwy 1 7428 Sky Hwy 1 6110 Rock Ridge 1 2823 Weaver Lane 1 2950 MLK Ave 1 3492 Flute Lane 1 9633 Rock Hwy 1 4231 3rd Ave 1 2193 4th Ridge 1 7069 4th Hwy 1 4885 Oak Lane 1 7693 Cherokee Lane 1 8639 5th Hwy 1 4242 Rock Lane 1 9317 Apache Ave 1 1515 Embaracadero St 1 9103 MLK Lane 1 2352 Sky Drive 1 6443 Washington Ridge 1 5380 Pine St 1 6479 Francis Ave 1 8879 1st Drive 1 3087 Oak Hwy 1 2199 Texas Drive 1 2204 Washington Lane 1 1437 3rd Lane 1 9744 Texas Drive 1 2865 Maple Lane 1 1699 Oak Drive 1 9236 2nd Hwy 1 4538 Flute Hwy 1 7780 Flute Lane 1 1529 Elm Ridge 1 2617 Andromedia Drive 1 7923 Elm Ave 1 9070 Tree Ave 1 7121 Britain Drive 1 6068 2nd St 1 5201 Texas Hwy 1 1914 Francis St 1 9169 Cherokee Hwy 1 5506 Best St 1 1248 MLK Ridge 1 6731 Andromedia Hwy 1 9020 Elm Ave 1 6058 Andromedia Hwy 1 8492 Weaver Hwy 1 3567 4th Drive 1 1030 Pine Lane 1 3311 2nd Drive 1 4390 4th Drive 1 7877 Sky Lane 1 9742 5th Ridge 1 8188 Tree Ave 1 1655 Francis Hwy 1 1555 Washington Lane 1 4431 Rock St 1 6634 Texas Ridge 1 3982 Washington Hwy 1 7582 Pine Drive 1 5812 Weaver Ave 1 5058 4th Lane 1 5790 Flute Ridge 1 7082 Oak Ridge 1 6440 Rock Lane 1 5630 1st Drive 1 9737 Solo Hwy 1 1598 3rd Drive 1 9910 Maple Ave 1 7042 Maple Ridge 1 5312 Francis Ridge 1 3220 Rock Drive 1 2381 1st Hwy 1 7973 4th St 1 6158 Sky Ridge 1 9138 3rd St 1 9239 Washington Ridge 1 5868 Sky Hwy 1 5071 1st Lane 1 8097 Maple Lane 1 5178 Weaver Hwy 1 8576 Andromedia St 1 9066 Best Ridge 1 3884 Pine Lane 1 9439 MLK St 1 3122 Apache Drive 1 6515 Oak Lane 1 3846 4th Hwy 1 7576 Pine Ridge 1 2078 3rd Ave 1 4414 Solo Drive 1 3639 Flute Hwy 1 8766 Lincoln Lane 1 3443 Maple Ridge 1 7253 MLK St 1 8964 Francis St 1 4857 Weaver St 1 6702 Andromedia St 1 6309 5th Ave 1 2577 Texas Ridge 1 3555 Francis Ridge 1 6957 Weaver Drive 1 5007 Oak St 1 9358 Texas Ridge 1 1346 5th Lane 1 7859 4th Ridge 1 1762 Maple Hwy 1 5022 1st St 1 6638 Tree Drive 1 3818 Texas Ridge 1 4687 5th Drive 1 7570 Cherokee Drive 1 9417 Tree Hwy 1 3770 Flute Drive 1 7601 Andromedia Lane 1 6770 1st St 1 5053 Tree Drive 1 3982 Weaver Lane 1 9153 3rd Hwy 1 8602 Washington Ridge 1 9603 Texas Lane 1 9422 Washington Ridge 1 3486 Flute Ave 1 6355 4th Hwy 1 6971 Best Ridge 1 4519 Embaracadero St 1 9728 Britain Hwy 1 3263 Pine Ridge 1 2430 MLK Ave 1 7041 Tree Ridge 1 2651 MLK Lane 1 2086 Francis Drive 1 4995 Weaver Ridge 1 4939 Oak Lane 1 5608 Solo St 1 1275 4th Ridge 1 2832 Andromedia Lane 1 4699 Texas Ridge 1 7628 4th Lane 1 6530 Weaver Ave 1 3659 Oak Lane 1 7488 Lincoln Lane 1 1218 Sky Hwy 1 2324 Texas Ridge 1 6536 MLK Hwy 1 3872 5th Drive 1 8618 Texas Lane 1 3753 Francis Lane 1 7551 Britain Lane 1 5499 Flute Ridge 1 4721 Cherokee Hwy 1 6985 Maple Lane 1 8021 Flute Ave 1 4574 Britain Hwy 1 9847 Elm St 1 8353 Britain Ridge 1 4053 Sky Lane 1 4653 Pine St 1 6550 Andromedia St 1 3929 Oak Drive 1 6719 Flute St 1 3127 Flute St 1 1213 4th Lane 1 7331 Sky Hwy 1 4983 MLK Ridge 1 6619 Flute Ave 1 5650 Rock Ave 1 7476 4th St 1 8957 Weaver Drive 1 8245 4th Hwy 1 4475 Lincoln Ridge 1 7121 Francis Lane 1 3029 5th Ave 1 2333 Maple Lane 1 5806 Embaracadero St 1 5765 Washington St 1 5071 Flute Ridge 1 7583 Washington Ave 1 5363 Weaver Lane 1 1512 Rock Lane 1 3814 Britain Drive 1 5812 3rd Hwy 1 8368 Cherokee Ave 1 4296 Pine Hwy 1 7575 Pine St 1 2337 Lincoln Hwy 1 9322 Rock Hwy 1 9007 Francis Hwy 1 8336 1st Ridge 1 2873 Flute Ave 1 9214 Texas Drive 1 4394 Oak St 1 8150 Washington Ridge 1 5622 Best Ridge 1 6045 Andromedia St 1 2905 Embaracadero Drive 1 7544 Washington Ave 1 7155 Apache Drive 1 6359 MLK Ridge 1 4965 MLK Drive 1 1091 1st Drive 1 8381 Solo Hwy 1 4234 Cherokee Lane 1 7299 Apache St 1 7733 Britain Lane 1 8096 Apache Hwy 1 1705 Weaver St 1 7061 Cherokee Drive 1 8211 Sky Hwy 1 2753 Cherokee Ave 1 5333 MLK Lane 1 1422 Flute Ave 1 8101 3rd Ridge 1 5821 2nd St 1 4905 Best Lane 1 4447 Francis Hwy 1 3706 4th Hwy 1 8809 Flute St 1 1840 Embaracadero Ave 1 3847 Elm Hwy 1 5621 4th Ave 1 3508 Washington St 1 4058 Tree Drive 1 2500 Tree St 1 3706 Texas Hwy 1 6608 MLK Hwy 1 5093 Flute Lane 1 3834 Pine St 1 6309 Cherokee Ave 1 2123 Texas Ave 1 8078 Britain Hwy 1 2492 Lincoln Lane 1 5168 5th Ave 1 2005 Texas Hwy 1 1818 Tree St 1 7108 Tree St 1 7797 Tree Ridge 1 6956 Maple Drive 1 9798 Sky Ridge 1 3707 Oak Ridge 1 7816 MLK Lane 1 9154 MLK Hwy 1 3052 Weaver Ridge 1 3751 Tree Hwy 1 5160 2nd Hwy 1 1320 Flute Lane 1 7615 Weaver Drive 1 6655 5th Drive 1 6888 Elm Ridge 1 7693 Britain Lane 1 2217 Tree Lane 1 8742 4th St 1 4239 Weaver Ave 1 8085 Andromedia St 1 2533 Elm St 1 1824 5th Lane 1 1809 Sky St 1 9748 Sky Drive 1 6660 MLK Drive 1 2850 Washington St 1 8493 Apache Drive 1 4486 Cherokee Ridge 1 2289 Weaver Ridge 1 7477 MLK Drive 1 3697 Apache Drive 1 7142 5th Lane 1 9682 Cherokee Ridge 1 6889 Cherokee St 1 9078 Francis Ridge 1 8917 Tree Ridge 1 2494 Andromedia Drive 1 5028 Maple Ridge 1 6035 Rock Ave 1 3246 Britain Ridge 1 9067 Texas Ave 1 4672 MLK St 1 7630 Rock Drive 1 4107 MLK Ridge 1 7644 Tree Ridge 1 3028 5th St 1 9633 MLK Lane 1 6931 Elm St 1 5475 Rock Lane 1 1454 5th Ridge 1 3915 Embaracadero St 1 3275 Pine St 1 4264 Lincoln Ridge 1 3422 Flute St 1 8212 Rock Ave 1 3447 Solo Ave 1 2215 Best Ave 1 9373 Pine Hwy 1 4268 2nd Ave 1 1956 Apache St 1 1628 Best Drive 1 2087 Apache Ave 1 8125 Texas Ridge 1 3320 5th Hwy 1 9724 Maple St 1 9484 Pine Drive 1 7976 Britain Drive 1 1507 Solo Ave 1 3952 Andromedia Lane 1 3841 Washington Lane 1 5236 Weaver Drive 1 9101 2nd Hwy 1 4872 Rock Ridge 1 1953 Sky Lane 1 3664 Francis Ridge 1 8864 Tree Ridge 1 2798 1st Ave 1 8822 Sky St 1 7701 Tree St 1 7785 Lincoln Lane 1 7552 3rd St 1 7327 Lincoln Drive 1 9352 Washington Ave 1 1469 Lincoln Drive 1 1910 Sky Ave 1 3769 Sky St 1 4866 4th Hwy 1 7684 Francis Ridge 1 8100 3rd Ave 1 8926 Texas Ridge 1 4633 5th Lane 1 3809 Texas Lane 1 9177 Texas Ave 1 8214 Flute St 1 8579 Apache Drive 1 3288 Tree Lane 1 2037 5th Drive 1 4780 Best Drive 1 1880 Weaver Drive 1 7835 Cherokee Hwy 1 6603 Francis Hwy 1 1102 Apache Hwy 1 5678 Lincoln Drive 1 7819 2nd Ave 1 9081 Cherokee Hwy 1 3167 2nd St 1 4693 Lincoln Hwy 1 6451 1st Hwy 1 1220 MLK Ave 1 7909 Andromedia Hwy 1 4652 Flute Drive 1 7504 Flute Drive 1 1364 Best St 1 1388 Embaracadero Hwy 1 5874 1st Hwy 1 5341 5th Ave 1 6137 MLK St 1 3289 Britain Drive 1 8782 3rd St 1 1830 Sky St 1 4994 Lincoln Drive 1 8492 Andromedia Ridge 1 9082 3rd Lane 1 5779 2nd Lane 1 1365 Francis Ave 1 4176 Britain Hwy 1 1087 Flute Drive 1 3930 Embaracadero St 1 4614 MLK Ave 1 5901 Elm Drive 1 4755 Best Lane 1 1989 Solo Lane 1 2777 Solo Drive 1 9935 4th Drive 1 4567 Pine Ave 1 9034 Weaver Ridge 1 3100 Best St 1 3495 Britain Drive 1 6375 2nd Lane 1 8897 Sky St 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 3653 Elm Drive 1 1985 5th Ave 1 6408 Weaver Ridge 1 3172 Tree Ridge 1 6658 Weaver St 1 2986 MLK Drive 1 2100 Francis Drive 1 2878 Britain Hwy 1 3006 Lincoln Ridge 1 6582 Elm Lane 1 5925 Tree Hwy 1 2697 Oak Drive 1 8954 Apache Lane 1 4615 Embaracadero Ave 1 7825 1st Ridge 1 2290 4th Ave 1 3995 Lincoln Hwy 1 7098 Lincoln Hwy 1 3492 Britain St 1 3592 MLK Ridge 1 3903 Oak Ave 1 5783 Oak Ave 1 6179 3rd Ridge 1 7314 Tree Drive 1 2808 Elm St 1 4020 Best Drive 1 9821 Francis Ave 1 3104 Sky Drive 1 7112 Weaver Ave 1 7740 MLK St 1 1532 Washington St 1 9751 Tree St 1 4554 Sky Ave 1 6574 4th Drive 1 6191 Oak Lane 1 5318 5th Ave 1 5445 Tree Hwy 1 9719 4th Lane 1 9245 Weaver Ridge 1 8198 Embaracadero Lane 1 9279 Oak Hwy 1 2280 4th Ave 1 1353 Washington St 1 1553 Lincoln St 1 8459 Apache Ave 1 5276 2nd Lane 1 5602 Britain St 1 3618 Sky Ave 1 9038 2nd Lane 1 2849 Pine Drive 1 5855 Apache St 1 8538 Texas Lane 1 2003 Maple Hwy 1 8081 Flute Ridge 1 2980 Sky Ridge 1 5865 Sky Lane 1 1358 Maple St 1 6939 3rd Hwy 1 6677 Andromedia Drive 1 9573 Weaver Ave 1 3658 Rock Drive 1 8524 Pine Lane 1 7717 Britain Hwy 1 3926 Rock Lane 1 1126 Texas Hwy 1 6678 Weaver Drive 1 2457 Washington Ave 1 6117 4th Ave 1 4055 2nd Drive 1 9988 Rock Ridge 1 1123 5th Lane 1 8306 1st Drive 1 9875 MLK Ave 1 7197 2nd Drive 1 7168 Andromedia Ridge 1 9730 2nd Hwy 1 8617 Best Ave 1 9980 Lincoln Ave 1 8876 1st St 1 2014 Rock Ave 1 1012 5th Lane 1 1833 Solo Ave 1 7649 Texas St 1 5667 4th Drive 1 1546 Cherokee Ave 1 4972 Francis Lane 1 4627 Elm Ridge 1 6738 Francis Hwy 1 3540 Maple St 1 9610 Cherokee St 1 4558 3rd Hwy 1 2644 MLK Drive 1 3419 Apache St 1 6331 MLK Ave 1 8946 2nd Drive 1 6384 5th Ridge 1 1643 Washington Hwy 1 2968 Andromedia Ave 1 4862 Lincoln Hwy 1 3900 Texas St 1 7897 Lincoln St 1 7529 Solo Ridge 1 3998 4th Hwy 1 5383 Maple Drive 1 4477 5th Ave 1 2603 Andromedia Hwy 1 1815 Cherokee Drive 1 1589 Pine St 1 8006 Maple Hwy 1 3110 Lincoln Lane 1 7846 Andromedia Drive 1 1845 Best St 1 4116 Embaracadero Lane 1 2577 Washington Drive 1 6838 Flute Lane 1 7135 Flute Lane 1 8991 Embaracadero Ave 1 4546 Tree St 1 6357 Texas Lane 1 1331 Britain Hwy 1 9911 Britain Lane 1 8758 5th St 1 5650 Sky Drive 1 2123 MLK Ridge 1 4981 Weaver St 1 5894 Flute Drive 1 7523 Oak Lane 1 1617 Rock Drive 1 2063 Weaver St 1 7295 Tree Hwy 1 6608 Apache Lane 1 8489 Pine Hwy 1 4095 MLK St 1 3097 4th Drive 1 5780 4th Ave 1 8456 1st Ave 1 7709 Rock Lane 1 9818 Cherokee Ave 1 8064 4th Ave 1 7405 Oak St 1 8542 Lincoln Ridge 1 2711 Britain Ave 1 9148 4th Hwy 1 6315 2nd Lane 1 7609 Rock St 1 1707 Sky Ave 1 6399 Oak Drive 1 1316 Britain Ridge 1 3094 Best Lane 1 6522 Apache Drive 1 5846 Weaver Drive 1 2526 Embaracadero Ave 1 6706 Francis Drive 1 4143 Maple Ridge 1 1957 Washington Ave 1 7954 Tree Ridge 1 3221 Solo Ridge 1 1135 Solo Lane 1 1992 Britain Drive 1 2230 1st St 1 9489 3rd St 1 4545 4th Ridge 1 3089 Oak Ridge 1 2299 Britain Drive 1 7178 Best Drive 1 5224 5th Lane 1 9633 4th St 1 3726 MLK Hwy 1 6409 Cherokee Drive 1 9720 Lincoln Hwy 1 1472 4th Drive 1 2757 4th Hwy 1 7782 Rock St 1 7791 Britain Ridge 1 6738 Washington Hwy 1 7397 4th Drive 1 3376 5th Drive 1 6493 Lincoln Lane 1 7705 Lincoln Drive 1 8118 Elm Ridge 1 5640 Embaracadero Lane 1 3835 5th Ave 1 8167 Apache Ave 1 5997 Embaracadero Drive 1 4214 MLK Ridge 1 9878 Washington Ave 1 6011 Britain St 1 4782 Sky Lane 1 2376 Sky Ridge 1 5074 3rd St 1 1267 Francis Hwy 1 5985 Lincoln Lane 1 6724 Andromedia St 1 7900 Sky Hwy 1 7236 Apache Lane 1 9942 Tree Ave 1 1328 Texas Lane 1 5226 Maple St 1 1725 Solo Lane 1 3929 Elm Ave 1 5924 Maple Drive 1 3418 Texas Lane 1 7393 Washington St 1 9685 Sky Ridge 1 1562 Britain St 1 6604 Apache Drive 1 4119 Texas St 1 1386 Britain St 1 7721 Washington Ridge 1 7930 Texas Ave 1 7002 Oak Hwy 1 3167 4th Ridge 1 5474 Weaver Hwy 1 5812 Oak St 1 1331 Elm Ridge 1 3808 5th Ave 1 6676 Tree Lane 1 7756 Pine Hwy 1 6128 Elm Lane 1 6256 Elm St 1 6684 Solo Lane 1 3656 Solo Ave 1 7574 4th St 1 8749 Tree St 1 6250 1st Ridge 1 8920 Best Ave 1 4955 Lincoln Ridge 1 8668 Flute St 1 8014 Embaracadero Drive 1 7819 Oak St 1 6501 5th Drive 1 3327 Lincoln Drive 1 7495 Washington Ave 1 2756 Britain Hwy 1 5769 Texas Lane 1 6859 Flute Ridge 1 5532 Weaver Ridge 1 9240 Britain Ave 1 6428 Andromedia Lane 1 9523 Solo Hwy 1 1376 Pine St 1 5771 Best St 1 7629 5th St 1 2253 Maple Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 4539 Texas St 1 4826 5th St 1 2311 4th St 1 2469 Francis Lane 1 3966 Oak Hwy 1 6874 Maple Ridge 1 5277 Texas Lane 1 4577 Sky Hwy 1 8233 Tree Drive 1 4291 Sky Hwy 1 1916 Elm St 1 1491 Francis Ridge 1 6329 Apache Ave 1 9760 Solo Lane 1 6853 Sky Hwy 1 1923 2nd Hwy 1 4335 1st St 1 8834 Elm Drive 1 4098 Weaver Ridge 1 2900 Sky Drive 1 1269 Flute Drive 1 5778 Pine Ridge 1 9224 Sky Drive 1 5499 Elm Hwy 1 4931 Maple Drive 1 2774 Apache Drive 1 9562 4th Ridge 1 1298 Maple Hwy 1 7877 3rd Ridge 1 6303 1st Drive 1 5051 Elm St 1 6934 Lincoln Ave 1 2397 Cherokee Ave 1 7281 Maple Hwy 1 1229 5th Ave 1 6494 4th Ave 1 7745 Washington Ridge 1 6981 Weaver St 1 1325 1st Lane 1 5862 Apache Ridge 1 8212 Flute Ridge 1 1654 Pine St 1 3743 Andromedia Ridge 1 6390 Apache St 1 8991 Texas Hwy 1 3771 4th St 1 3066 Francis Ave 1 4702 Texas Drive 1 7238 2nd St 1 5280 Pine Ave 1 9611 Pine Ridge 1 6206 3rd Ridge 1 9397 Francis St 1 8442 Britain Hwy 1 4981 Flute Hwy 1 4453 Best Ave 1 8080 Oak Lane 1 9214 Elm Ridge 1 4434 Lincoln Ave 1 6723 Best Drive 1 2117 Lincoln Hwy 1 8983 Tree St 1 4347 2nd Ridge 1 9418 5th Hwy 1 8655 Cherokee Lane 1 4937 Flute Drive 1 7240 5th Ridge 1 5585 Washington Drive 1 5743 4th Ridge 1 1371 Texas Lane 1 9529 4th Drive 1 9316 Pine Ave 1 1741 Best Ridge 1 8586 1st Ridge 1 2537 5th Ave 1 5914 Oak Ave 1 8624 Francis Ave 1 5771 Sky Ave 1 5868 Best Drive 1 8203 Lincoln Ave 1 7773 Tree Hwy 1 1173 Andromedia Ave 1 6484 Tree Drive 1 6851 3rd Drive 1 6741 Oak Ridge 1 1589 Best Ave 1 9397 5th Hwy 1 7316 Texas Ave 1 7201 Washington Ave 1 1381 Francis Ave 1 8718 Apache Lane 1 1416 Cherokee Ridge 1 1580 Maple Lane 1 8269 Sky Hwy 1 3323 1st Lane 1 9751 Sky Ridge 1 3092 Texas Drive 1 4814 Lincoln Lane 1 1681 Cherokee Hwy 1 8845 5th Ave 1 8821 Elm St 1 2725 Britain Ridge 1 9588 Solo St 1 6618 Cherokee Drive 1 7511 1st Ave 1 6583 MLK Ridge 1 4460 4th Lane 1 7571 Elm Ridge 1 3693 Pine Ave 1 3660 Andromedia Hwy 1 6605 Tree Ave 1 6067 Weaver Ridge 1 3227 Maple Ave 1 8667 Weaver Lane 1 4671 5th Ridge 1 9488 Best Drive 1 6378 Britain Ave 1 5782 Rock Drive 1 6092 5th Ave 1 9293 Pine Lane 1 4175 Elm Ridge 1 7144 Andromedia St 1 7121 Rock St 1 6945 Texas Hwy 1 1578 5th Lane 1 8049 4th St 1 2804 Best St 1 4496 Pine Lane 1 6681 Texas Ridge 1 5249 4th Ave 1 1620 Oak Ave 1 4625 MLK Drive 1 6435 Texas Ave 1 9760 4th Hwy 1 3966 Francis Ridge 1 4985 Sky Lane 1 2275 Best Lane 1 7500 Texas Ridge 1 1929 Britain Drive 1 5663 Oak Lane 1 8770 1st Lane 1 2886 Tree Ridge 1 3041 3rd Ave 1 6467 Best Ave 1 8811 Maple Hwy 1 6193 1st Hwy 1 9929 Rock Drive 1 2903 Weaver Drive 1 8907 Tree Ave 1 2820 Britain St 1 9580 MLK Ave 1 2201 4th Lane 1 9856 Apache St 1 6165 Rock Ridge 1 5260 Francis Drive 1 5480 3rd Ridge 1 4642 Rock Ridge 1 4188 Britain Ave 1 5838 Pine Lane 1 8621 Best Ridge 1 8906 Elm Lane 1 4585 Francis Ave 1 5279 Pine Ridge 1 5839 Weaver Lane 1 5586 2nd St 1 4254 Best Ridge 1 3053 Lincoln Drive 1 3397 5th Ave 1 1951 Best Ave 1 7535 5th Lane 1 8917 Cherokee Lane 1 1536 Flute Drive 1 1028 Sky Lane 1 2668 Cherokee St 1 6717 Best Drive 1 1806 Weaver Ridge 1 8477 Francis Hwy 1 9369 Flute Hwy 1 2696 Cherokee Ridge 1 3184 Oak Ave 1 3282 4th Lane 1 4964 Elm Lane 1 8404 Embaracadero St 1 7783 Lincoln Hwy 1 3553 Texas Ave 1 7928 Maple Ridge 1 6259 Weaver St 1 7380 5th Hwy 1 8689 Maple Hwy 1 8704 Britain Lane 1 5455 Oak Hwy 1 3039 Oak Hwy 1 7066 Texas Ave 1 5211 Weaver Drive 1 5540 Sky St 1 4910 1st Lane 1 3171 Andromedia Lane 1 7162 Maple Ave 1 6429 4th Hwy 1 5191 4th St 1 7534 MLK Hwy 1 5969 Francis St 1 4793 4th Ridge 1 6751 5th Hwy 1 8548 Cherokee Ridge 1 4907 Andromedia Drive 1 8949 Rock Hwy 1 1540 Apache Lane 1 6492 4th Lane 1 2509 Rock Drive 1 2654 Embaracadero St 1 5124 Maple St 1 1687 3rd Lane 1 2306 5th Lane 1 4369 Maple Lane 1 1273 Rock Lane 1 6955 Pine Drive 1 8983 Francis Ridge 1 3177 MLK Ridge 1 5584 Britain Lane 1 9278 Francis Ridge 1 1810 Elm Hwy 1 3488 Flute Lane 1 4154 Lincoln Hwy 1 2646 MLK Drive 1 9325 Lincoln Drive 1 8204 Pine Lane 1 5269 Flute Hwy 1 2539 Embaracadero Ridge 1 9286 Oak Ave 1 7502 Rock Lane 1 4755 1st St 1 9573 2nd Ave 1 1240 Tree Lane 1 6259 Lincoln Hwy 1 2318 Washington Hwy 1 3416 Washington Drive 1 5994 5th Ave 1 4905 Francis Ave 1 5649 Texas Ave 1 8940 Elm Ave 1 5904 1st Drive 1 5352 Lincoln Drive 1 3590 Best Hwy 1 8941 Solo Ridge 1 7460 Apache Lane 1 9169 Pine Ridge 1 7281 Oak St 1 8701 5th Lane 1 9138 1st St 1 3550 Washington Ave 1 2003 2nd Hwy 1 4434 Weaver St 1 4279 Solo Drive 1 2644 Elm Drive 1 2862 Tree Ridge 1 9109 Britain Drive 1 6456 Andromedia Drive 1 9816 Britain St 1 9879 Apache Drive 1 5189 Francis Drive 1 2914 Oak Drive 1 4876 Washington Drive 1 7426 Rock Drive 1 8973 Washington St 1 1832 Elm Hwy 1 3525 3rd Hwy 1 2654 Elm Drive 1 1679 2nd Hwy 1 4158 Washington Lane 1 3878 Tree Lane 1 3998 Flute St 1 5431 3rd Ridge 1 2889 Francis St 1 3790 Andromedia Hwy 1 7459 Flute St 1 8832 Pine Drive 1 5483 Francis Drive 1 5532 Francis Lane 1 9639 Britain Ridge 1 8995 1st Ave 1 7828 Cherokee Ave 1 2920 5th Ave 1 2100 MLK St 1 8509 Apache St 1 4629 Elm Ridge 1 3457 Texas Lane 1 1941 5th Ridge 1 8862 Maple Ridge 1 6149 Best Ridge 1 6317 Best St 1 4429 Washington St 1 4618 Flute Ave 1 2293 Washington Ave 1 2299 1st St 1 3797 Solo Lane 1 6848 Elm Hwy 1 1186 Rock St 1 5719 2nd Lane 1 1515 Pine Lane 1 7705 Best Ridge 1 2809 Francis Lane 1 8215 Flute Drive 1 3936 Tree Drive 1 1128 Maple Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.incident_severity.value_counts()
Minor Damage 354 Total Loss 280 Major Damage 276 Trivial Damage 90 Name: incident_severity, dtype: int64
fraud_con['incident_severity'] = fraud_con['incident_severity'].astype('category')
fraud_con['incident_severity'] = fraud_con['incident_severity'].cat.codes
fraud_con.incident_severity.value_counts()
1 354 2 280 0 276 3 90 Name: incident_severity, dtype: int64
fraud_con.incident_state.value_counts()
NY 262 SC 248 WV 217 NC 110 VA 110 PA 30 OH 23 Name: incident_state, dtype: int64
fraud_con['incident_state'] = fraud_con['incident_state'].astype('category')
fraud_con['incident_state'] = fraud_con['incident_state'].cat.codes
fraud_con.incident_state.value_counts()
1 262 4 248 6 217 5 110 0 110 3 30 2 23 Name: incident_state, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 12-10-1995 1 11-09-2010 1 25-01-2008 1 28-10-1998 1 11-03-2014 1 25-12-2001 1 10-11-1992 1 28-01-1992 1 06-05-2002 1 07-02-2007 1 01-10-2010 1 10-02-1997 1 19-12-2004 1 29-07-1995 1 15-05-2000 1 04-03-2006 1 18-01-2014 1 21-05-2000 1 19-05-1990 1 24-09-1992 1 05-08-2014 1 22-02-1992 1 22-06-2009 1 02-12-2011 1 18-02-1995 1 28-01-1993 1 24-06-1998 1 27-05-1994 1 26-06-2009 1 01-03-1997 1 29-11-1999 1 05-12-2013 1 13-05-2001 1 16-09-2013 1 16-09-1990 1 25-05-2002 1 09-11-1992 1 13-07-2013 1 08-05-2005 1 26-08-2000 1 07-12-2001 1 22-03-1992 1 26-09-2010 1 17-05-1994 1 04-06-1996 1 22-10-2011 1 19-05-1992 1 09-04-2001 1 04-05-1995 1 22-07-2004 1 17-02-2009 1 08-04-1996 1 11-12-1992 1 21-06-1994 1 01-10-2001 1 29-09-2001 1 11-07-2007 1 29-02-2004 1 13-04-2006 1 01-10-2006 1 29-01-1993 1 28-05-2014 1 19-12-1997 1 04-02-1992 1 10-10-2005 1 21-09-1990 1 26-11-2001 1 28-10-1997 1 20-11-2005 1 05-07-2007 1 14-04-1993 1 03-01-2015 1 18-06-1997 1 28-04-2013 1 03-11-2009 1 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21-02-2015 19 23-02-2015 19 01-01-2015 19 14-01-2015 19 12-01-2015 19 21-01-2015 19 22-02-2015 20 06-02-2015 20 12-02-2015 20 31-01-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 24-01-2015 24 10-01-2015 24 04-02-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_LOCATION : 1000 9043 Maple Hwy 1 3414 Elm Ave 1 6581 Rock Ridge 1 3654 Cherokee Ave 1 3340 3rd Hwy 1 6668 Andromedia Ridge 1 8638 3rd Ave 1 5104 Francis Drive 1 9787 Andromedia Ave 1 5639 1st Ridge 1 9706 MLK Lane 1 2352 MLK Drive 1 9360 3rd Drive 1 3439 Andromedia Hwy 1 9105 Tree Lane 1 2048 3rd Ridge 1 5061 Francis Ave 1 8281 Lincoln Lane 1 6044 Weaver Drive 1 4710 Lincoln Hwy 1 1879 4th Lane 1 9734 2nd Ridge 1 1133 Apache St 1 7447 Lincoln Ridge 1 9918 Andromedia Drive 1 1306 Andromedia St 1 3777 Maple Ave 1 8453 Elm St 1 1215 Pine Hwy 1 5455 Tree Ridge 1 2733 Texas Drive 1 6104 Oak Ave 1 3799 Embaracadero Drive 1 4237 4th St 1 6834 1st Drive 1 7466 MLK Ridge 1 3423 Francis Ave 1 1110 4th Drive 1 1738 Solo Lane 1 3098 Oak Lane 1 4492 Andromedia Ave 1 3842 Solo Ridge 1 2942 1st Lane 1 7756 Solo Drive 1 8872 Oak Ridge 1 4939 Best St 1 2220 1st Lane 1 2787 MLK St 1 3796 Cherokee Drive 1 4835 Britain Ridge 1 9657 5th Ave 1 5971 5th Hwy 1 4232 Britain Ridge 1 6260 5th Lane 1 8805 Cherokee Drive 1 3805 Lincoln Hwy 1 1558 1st Ridge 1 3925 Sky St 1 6751 Pine Ridge 1 5418 Britain Ave 1 9794 Embaracadero St 1 7434 Oak Hwy 1 6516 Solo Drive 1 5459 MLK Ave 1 2889 Weaver St 1 3618 Maple Lane 1 4272 Oak Ridge 1 2272 Embaracadero Drive 1 7223 Embaracadero St 1 3102 Apache St 1 1919 4th Lane 1 1618 Maple Hwy 1 3196 Cherokee St 1 3061 Francis Hwy 1 7428 Sky Hwy 1 6110 Rock Ridge 1 2823 Weaver Lane 1 2950 MLK Ave 1 3492 Flute Lane 1 9633 Rock Hwy 1 4231 3rd Ave 1 2193 4th Ridge 1 7069 4th Hwy 1 4885 Oak Lane 1 7693 Cherokee Lane 1 8639 5th Hwy 1 4242 Rock Lane 1 9317 Apache Ave 1 1515 Embaracadero St 1 9103 MLK Lane 1 2352 Sky Drive 1 6443 Washington Ridge 1 5380 Pine St 1 6479 Francis Ave 1 8879 1st Drive 1 3087 Oak Hwy 1 2199 Texas Drive 1 2204 Washington Lane 1 1437 3rd Lane 1 9744 Texas Drive 1 2865 Maple Lane 1 1699 Oak Drive 1 9236 2nd Hwy 1 4538 Flute Hwy 1 7780 Flute Lane 1 1529 Elm Ridge 1 2617 Andromedia Drive 1 7923 Elm Ave 1 9070 Tree Ave 1 7121 Britain Drive 1 6068 2nd St 1 5201 Texas Hwy 1 1914 Francis St 1 9169 Cherokee Hwy 1 5506 Best St 1 1248 MLK Ridge 1 6731 Andromedia Hwy 1 9020 Elm Ave 1 6058 Andromedia Hwy 1 8492 Weaver Hwy 1 3567 4th Drive 1 1030 Pine Lane 1 3311 2nd Drive 1 4390 4th Drive 1 7877 Sky Lane 1 9742 5th Ridge 1 8188 Tree Ave 1 1655 Francis Hwy 1 1555 Washington Lane 1 4431 Rock St 1 6634 Texas Ridge 1 3982 Washington Hwy 1 7582 Pine Drive 1 5812 Weaver Ave 1 5058 4th Lane 1 5790 Flute Ridge 1 7082 Oak Ridge 1 6440 Rock Lane 1 5630 1st Drive 1 9737 Solo Hwy 1 1598 3rd Drive 1 9910 Maple Ave 1 7042 Maple Ridge 1 5312 Francis Ridge 1 3220 Rock Drive 1 2381 1st Hwy 1 7973 4th St 1 6158 Sky Ridge 1 9138 3rd St 1 9239 Washington Ridge 1 5868 Sky Hwy 1 5071 1st Lane 1 8097 Maple Lane 1 5178 Weaver Hwy 1 8576 Andromedia St 1 9066 Best Ridge 1 3884 Pine Lane 1 9439 MLK St 1 3122 Apache Drive 1 6515 Oak Lane 1 3846 4th Hwy 1 7576 Pine Ridge 1 2078 3rd Ave 1 4414 Solo Drive 1 3639 Flute Hwy 1 8766 Lincoln Lane 1 3443 Maple Ridge 1 7253 MLK St 1 8964 Francis St 1 4857 Weaver St 1 6702 Andromedia St 1 6309 5th Ave 1 2577 Texas Ridge 1 3555 Francis Ridge 1 6957 Weaver Drive 1 5007 Oak St 1 9358 Texas Ridge 1 1346 5th Lane 1 7859 4th Ridge 1 1762 Maple Hwy 1 5022 1st St 1 6638 Tree Drive 1 3818 Texas Ridge 1 4687 5th Drive 1 7570 Cherokee Drive 1 9417 Tree Hwy 1 3770 Flute Drive 1 7601 Andromedia Lane 1 6770 1st St 1 5053 Tree Drive 1 3982 Weaver Lane 1 9153 3rd Hwy 1 8602 Washington Ridge 1 9603 Texas Lane 1 9422 Washington Ridge 1 3486 Flute Ave 1 6355 4th Hwy 1 6971 Best Ridge 1 4519 Embaracadero St 1 9728 Britain Hwy 1 3263 Pine Ridge 1 2430 MLK Ave 1 7041 Tree Ridge 1 2651 MLK Lane 1 2086 Francis Drive 1 4995 Weaver Ridge 1 4939 Oak Lane 1 5608 Solo St 1 1275 4th Ridge 1 2832 Andromedia Lane 1 4699 Texas Ridge 1 7628 4th Lane 1 6530 Weaver Ave 1 3659 Oak Lane 1 7488 Lincoln Lane 1 1218 Sky Hwy 1 2324 Texas Ridge 1 6536 MLK Hwy 1 3872 5th Drive 1 8618 Texas Lane 1 3753 Francis Lane 1 7551 Britain Lane 1 5499 Flute Ridge 1 4721 Cherokee Hwy 1 6985 Maple Lane 1 8021 Flute Ave 1 4574 Britain Hwy 1 9847 Elm St 1 8353 Britain Ridge 1 4053 Sky Lane 1 4653 Pine St 1 6550 Andromedia St 1 3929 Oak Drive 1 6719 Flute St 1 3127 Flute St 1 1213 4th Lane 1 7331 Sky Hwy 1 4983 MLK Ridge 1 6619 Flute Ave 1 5650 Rock Ave 1 7476 4th St 1 8957 Weaver Drive 1 8245 4th Hwy 1 4475 Lincoln Ridge 1 7121 Francis Lane 1 3029 5th Ave 1 2333 Maple Lane 1 5806 Embaracadero St 1 5765 Washington St 1 5071 Flute Ridge 1 7583 Washington Ave 1 5363 Weaver Lane 1 1512 Rock Lane 1 3814 Britain Drive 1 5812 3rd Hwy 1 8368 Cherokee Ave 1 4296 Pine Hwy 1 7575 Pine St 1 2337 Lincoln Hwy 1 9322 Rock Hwy 1 9007 Francis Hwy 1 8336 1st Ridge 1 2873 Flute Ave 1 9214 Texas Drive 1 4394 Oak St 1 8150 Washington Ridge 1 5622 Best Ridge 1 6045 Andromedia St 1 2905 Embaracadero Drive 1 7544 Washington Ave 1 7155 Apache Drive 1 6359 MLK Ridge 1 4965 MLK Drive 1 1091 1st Drive 1 8381 Solo Hwy 1 4234 Cherokee Lane 1 7299 Apache St 1 7733 Britain Lane 1 8096 Apache Hwy 1 1705 Weaver St 1 7061 Cherokee Drive 1 8211 Sky Hwy 1 2753 Cherokee Ave 1 5333 MLK Lane 1 1422 Flute Ave 1 8101 3rd Ridge 1 5821 2nd St 1 4905 Best Lane 1 4447 Francis Hwy 1 3706 4th Hwy 1 8809 Flute St 1 1840 Embaracadero Ave 1 3847 Elm Hwy 1 5621 4th Ave 1 3508 Washington St 1 4058 Tree Drive 1 2500 Tree St 1 3706 Texas Hwy 1 6608 MLK Hwy 1 5093 Flute Lane 1 3834 Pine St 1 6309 Cherokee Ave 1 2123 Texas Ave 1 8078 Britain Hwy 1 2492 Lincoln Lane 1 5168 5th Ave 1 2005 Texas Hwy 1 1818 Tree St 1 7108 Tree St 1 7797 Tree Ridge 1 6956 Maple Drive 1 9798 Sky Ridge 1 3707 Oak Ridge 1 7816 MLK Lane 1 9154 MLK Hwy 1 3052 Weaver Ridge 1 3751 Tree Hwy 1 5160 2nd Hwy 1 1320 Flute Lane 1 7615 Weaver Drive 1 6655 5th Drive 1 6888 Elm Ridge 1 7693 Britain Lane 1 2217 Tree Lane 1 8742 4th St 1 4239 Weaver Ave 1 8085 Andromedia St 1 2533 Elm St 1 1824 5th Lane 1 1809 Sky St 1 9748 Sky Drive 1 6660 MLK Drive 1 2850 Washington St 1 8493 Apache Drive 1 4486 Cherokee Ridge 1 2289 Weaver Ridge 1 7477 MLK Drive 1 3697 Apache Drive 1 7142 5th Lane 1 9682 Cherokee Ridge 1 6889 Cherokee St 1 9078 Francis Ridge 1 8917 Tree Ridge 1 2494 Andromedia Drive 1 5028 Maple Ridge 1 6035 Rock Ave 1 3246 Britain Ridge 1 9067 Texas Ave 1 4672 MLK St 1 7630 Rock Drive 1 4107 MLK Ridge 1 7644 Tree Ridge 1 3028 5th St 1 9633 MLK Lane 1 6931 Elm St 1 5475 Rock Lane 1 1454 5th Ridge 1 3915 Embaracadero St 1 3275 Pine St 1 4264 Lincoln Ridge 1 3422 Flute St 1 8212 Rock Ave 1 3447 Solo Ave 1 2215 Best Ave 1 9373 Pine Hwy 1 4268 2nd Ave 1 1956 Apache St 1 1628 Best Drive 1 2087 Apache Ave 1 8125 Texas Ridge 1 3320 5th Hwy 1 9724 Maple St 1 9484 Pine Drive 1 7976 Britain Drive 1 1507 Solo Ave 1 3952 Andromedia Lane 1 3841 Washington Lane 1 5236 Weaver Drive 1 9101 2nd Hwy 1 4872 Rock Ridge 1 1953 Sky Lane 1 3664 Francis Ridge 1 8864 Tree Ridge 1 2798 1st Ave 1 8822 Sky St 1 7701 Tree St 1 7785 Lincoln Lane 1 7552 3rd St 1 7327 Lincoln Drive 1 9352 Washington Ave 1 1469 Lincoln Drive 1 1910 Sky Ave 1 3769 Sky St 1 4866 4th Hwy 1 7684 Francis Ridge 1 8100 3rd Ave 1 8926 Texas Ridge 1 4633 5th Lane 1 3809 Texas Lane 1 9177 Texas Ave 1 8214 Flute St 1 8579 Apache Drive 1 3288 Tree Lane 1 2037 5th Drive 1 4780 Best Drive 1 1880 Weaver Drive 1 7835 Cherokee Hwy 1 6603 Francis Hwy 1 1102 Apache Hwy 1 5678 Lincoln Drive 1 7819 2nd Ave 1 9081 Cherokee Hwy 1 3167 2nd St 1 4693 Lincoln Hwy 1 6451 1st Hwy 1 1220 MLK Ave 1 7909 Andromedia Hwy 1 4652 Flute Drive 1 7504 Flute Drive 1 1364 Best St 1 1388 Embaracadero Hwy 1 5874 1st Hwy 1 5341 5th Ave 1 6137 MLK St 1 3289 Britain Drive 1 8782 3rd St 1 1830 Sky St 1 4994 Lincoln Drive 1 8492 Andromedia Ridge 1 9082 3rd Lane 1 5779 2nd Lane 1 1365 Francis Ave 1 4176 Britain Hwy 1 1087 Flute Drive 1 3930 Embaracadero St 1 4614 MLK Ave 1 5901 Elm Drive 1 4755 Best Lane 1 1989 Solo Lane 1 2777 Solo Drive 1 9935 4th Drive 1 4567 Pine Ave 1 9034 Weaver Ridge 1 3100 Best St 1 3495 Britain Drive 1 6375 2nd Lane 1 8897 Sky St 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 3653 Elm Drive 1 1985 5th Ave 1 6408 Weaver Ridge 1 3172 Tree Ridge 1 6658 Weaver St 1 2986 MLK Drive 1 2100 Francis Drive 1 2878 Britain Hwy 1 3006 Lincoln Ridge 1 6582 Elm Lane 1 5925 Tree Hwy 1 2697 Oak Drive 1 8954 Apache Lane 1 4615 Embaracadero Ave 1 7825 1st Ridge 1 2290 4th Ave 1 3995 Lincoln Hwy 1 7098 Lincoln Hwy 1 3492 Britain St 1 3592 MLK Ridge 1 3903 Oak Ave 1 5783 Oak Ave 1 6179 3rd Ridge 1 7314 Tree Drive 1 2808 Elm St 1 4020 Best Drive 1 9821 Francis Ave 1 3104 Sky Drive 1 7112 Weaver Ave 1 7740 MLK St 1 1532 Washington St 1 9751 Tree St 1 4554 Sky Ave 1 6574 4th Drive 1 6191 Oak Lane 1 5318 5th Ave 1 5445 Tree Hwy 1 9719 4th Lane 1 9245 Weaver Ridge 1 8198 Embaracadero Lane 1 9279 Oak Hwy 1 2280 4th Ave 1 1353 Washington St 1 1553 Lincoln St 1 8459 Apache Ave 1 5276 2nd Lane 1 5602 Britain St 1 3618 Sky Ave 1 9038 2nd Lane 1 2849 Pine Drive 1 5855 Apache St 1 8538 Texas Lane 1 2003 Maple Hwy 1 8081 Flute Ridge 1 2980 Sky Ridge 1 5865 Sky Lane 1 1358 Maple St 1 6939 3rd Hwy 1 6677 Andromedia Drive 1 9573 Weaver Ave 1 3658 Rock Drive 1 8524 Pine Lane 1 7717 Britain Hwy 1 3926 Rock Lane 1 1126 Texas Hwy 1 6678 Weaver Drive 1 2457 Washington Ave 1 6117 4th Ave 1 4055 2nd Drive 1 9988 Rock Ridge 1 1123 5th Lane 1 8306 1st Drive 1 9875 MLK Ave 1 7197 2nd Drive 1 7168 Andromedia Ridge 1 9730 2nd Hwy 1 8617 Best Ave 1 9980 Lincoln Ave 1 8876 1st St 1 2014 Rock Ave 1 1012 5th Lane 1 1833 Solo Ave 1 7649 Texas St 1 5667 4th Drive 1 1546 Cherokee Ave 1 4972 Francis Lane 1 4627 Elm Ridge 1 6738 Francis Hwy 1 3540 Maple St 1 9610 Cherokee St 1 4558 3rd Hwy 1 2644 MLK Drive 1 3419 Apache St 1 6331 MLK Ave 1 8946 2nd Drive 1 6384 5th Ridge 1 1643 Washington Hwy 1 2968 Andromedia Ave 1 4862 Lincoln Hwy 1 3900 Texas St 1 7897 Lincoln St 1 7529 Solo Ridge 1 3998 4th Hwy 1 5383 Maple Drive 1 4477 5th Ave 1 2603 Andromedia Hwy 1 1815 Cherokee Drive 1 1589 Pine St 1 8006 Maple Hwy 1 3110 Lincoln Lane 1 7846 Andromedia Drive 1 1845 Best St 1 4116 Embaracadero Lane 1 2577 Washington Drive 1 6838 Flute Lane 1 7135 Flute Lane 1 8991 Embaracadero Ave 1 4546 Tree St 1 6357 Texas Lane 1 1331 Britain Hwy 1 9911 Britain Lane 1 8758 5th St 1 5650 Sky Drive 1 2123 MLK Ridge 1 4981 Weaver St 1 5894 Flute Drive 1 7523 Oak Lane 1 1617 Rock Drive 1 2063 Weaver St 1 7295 Tree Hwy 1 6608 Apache Lane 1 8489 Pine Hwy 1 4095 MLK St 1 3097 4th Drive 1 5780 4th Ave 1 8456 1st Ave 1 7709 Rock Lane 1 9818 Cherokee Ave 1 8064 4th Ave 1 7405 Oak St 1 8542 Lincoln Ridge 1 2711 Britain Ave 1 9148 4th Hwy 1 6315 2nd Lane 1 7609 Rock St 1 1707 Sky Ave 1 6399 Oak Drive 1 1316 Britain Ridge 1 3094 Best Lane 1 6522 Apache Drive 1 5846 Weaver Drive 1 2526 Embaracadero Ave 1 6706 Francis Drive 1 4143 Maple Ridge 1 1957 Washington Ave 1 7954 Tree Ridge 1 3221 Solo Ridge 1 1135 Solo Lane 1 1992 Britain Drive 1 2230 1st St 1 9489 3rd St 1 4545 4th Ridge 1 3089 Oak Ridge 1 2299 Britain Drive 1 7178 Best Drive 1 5224 5th Lane 1 9633 4th St 1 3726 MLK Hwy 1 6409 Cherokee Drive 1 9720 Lincoln Hwy 1 1472 4th Drive 1 2757 4th Hwy 1 7782 Rock St 1 7791 Britain Ridge 1 6738 Washington Hwy 1 7397 4th Drive 1 3376 5th Drive 1 6493 Lincoln Lane 1 7705 Lincoln Drive 1 8118 Elm Ridge 1 5640 Embaracadero Lane 1 3835 5th Ave 1 8167 Apache Ave 1 5997 Embaracadero Drive 1 4214 MLK Ridge 1 9878 Washington Ave 1 6011 Britain St 1 4782 Sky Lane 1 2376 Sky Ridge 1 5074 3rd St 1 1267 Francis Hwy 1 5985 Lincoln Lane 1 6724 Andromedia St 1 7900 Sky Hwy 1 7236 Apache Lane 1 9942 Tree Ave 1 1328 Texas Lane 1 5226 Maple St 1 1725 Solo Lane 1 3929 Elm Ave 1 5924 Maple Drive 1 3418 Texas Lane 1 7393 Washington St 1 9685 Sky Ridge 1 1562 Britain St 1 6604 Apache Drive 1 4119 Texas St 1 1386 Britain St 1 7721 Washington Ridge 1 7930 Texas Ave 1 7002 Oak Hwy 1 3167 4th Ridge 1 5474 Weaver Hwy 1 5812 Oak St 1 1331 Elm Ridge 1 3808 5th Ave 1 6676 Tree Lane 1 7756 Pine Hwy 1 6128 Elm Lane 1 6256 Elm St 1 6684 Solo Lane 1 3656 Solo Ave 1 7574 4th St 1 8749 Tree St 1 6250 1st Ridge 1 8920 Best Ave 1 4955 Lincoln Ridge 1 8668 Flute St 1 8014 Embaracadero Drive 1 7819 Oak St 1 6501 5th Drive 1 3327 Lincoln Drive 1 7495 Washington Ave 1 2756 Britain Hwy 1 5769 Texas Lane 1 6859 Flute Ridge 1 5532 Weaver Ridge 1 9240 Britain Ave 1 6428 Andromedia Lane 1 9523 Solo Hwy 1 1376 Pine St 1 5771 Best St 1 7629 5th St 1 2253 Maple Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 4539 Texas St 1 4826 5th St 1 2311 4th St 1 2469 Francis Lane 1 3966 Oak Hwy 1 6874 Maple Ridge 1 5277 Texas Lane 1 4577 Sky Hwy 1 8233 Tree Drive 1 4291 Sky Hwy 1 1916 Elm St 1 1491 Francis Ridge 1 6329 Apache Ave 1 9760 Solo Lane 1 6853 Sky Hwy 1 1923 2nd Hwy 1 4335 1st St 1 8834 Elm Drive 1 4098 Weaver Ridge 1 2900 Sky Drive 1 1269 Flute Drive 1 5778 Pine Ridge 1 9224 Sky Drive 1 5499 Elm Hwy 1 4931 Maple Drive 1 2774 Apache Drive 1 9562 4th Ridge 1 1298 Maple Hwy 1 7877 3rd Ridge 1 6303 1st Drive 1 5051 Elm St 1 6934 Lincoln Ave 1 2397 Cherokee Ave 1 7281 Maple Hwy 1 1229 5th Ave 1 6494 4th Ave 1 7745 Washington Ridge 1 6981 Weaver St 1 1325 1st Lane 1 5862 Apache Ridge 1 8212 Flute Ridge 1 1654 Pine St 1 3743 Andromedia Ridge 1 6390 Apache St 1 8991 Texas Hwy 1 3771 4th St 1 3066 Francis Ave 1 4702 Texas Drive 1 7238 2nd St 1 5280 Pine Ave 1 9611 Pine Ridge 1 6206 3rd Ridge 1 9397 Francis St 1 8442 Britain Hwy 1 4981 Flute Hwy 1 4453 Best Ave 1 8080 Oak Lane 1 9214 Elm Ridge 1 4434 Lincoln Ave 1 6723 Best Drive 1 2117 Lincoln Hwy 1 8983 Tree St 1 4347 2nd Ridge 1 9418 5th Hwy 1 8655 Cherokee Lane 1 4937 Flute Drive 1 7240 5th Ridge 1 5585 Washington Drive 1 5743 4th Ridge 1 1371 Texas Lane 1 9529 4th Drive 1 9316 Pine Ave 1 1741 Best Ridge 1 8586 1st Ridge 1 2537 5th Ave 1 5914 Oak Ave 1 8624 Francis Ave 1 5771 Sky Ave 1 5868 Best Drive 1 8203 Lincoln Ave 1 7773 Tree Hwy 1 1173 Andromedia Ave 1 6484 Tree Drive 1 6851 3rd Drive 1 6741 Oak Ridge 1 1589 Best Ave 1 9397 5th Hwy 1 7316 Texas Ave 1 7201 Washington Ave 1 1381 Francis Ave 1 8718 Apache Lane 1 1416 Cherokee Ridge 1 1580 Maple Lane 1 8269 Sky Hwy 1 3323 1st Lane 1 9751 Sky Ridge 1 3092 Texas Drive 1 4814 Lincoln Lane 1 1681 Cherokee Hwy 1 8845 5th Ave 1 8821 Elm St 1 2725 Britain Ridge 1 9588 Solo St 1 6618 Cherokee Drive 1 7511 1st Ave 1 6583 MLK Ridge 1 4460 4th Lane 1 7571 Elm Ridge 1 3693 Pine Ave 1 3660 Andromedia Hwy 1 6605 Tree Ave 1 6067 Weaver Ridge 1 3227 Maple Ave 1 8667 Weaver Lane 1 4671 5th Ridge 1 9488 Best Drive 1 6378 Britain Ave 1 5782 Rock Drive 1 6092 5th Ave 1 9293 Pine Lane 1 4175 Elm Ridge 1 7144 Andromedia St 1 7121 Rock St 1 6945 Texas Hwy 1 1578 5th Lane 1 8049 4th St 1 2804 Best St 1 4496 Pine Lane 1 6681 Texas Ridge 1 5249 4th Ave 1 1620 Oak Ave 1 4625 MLK Drive 1 6435 Texas Ave 1 9760 4th Hwy 1 3966 Francis Ridge 1 4985 Sky Lane 1 2275 Best Lane 1 7500 Texas Ridge 1 1929 Britain Drive 1 5663 Oak Lane 1 8770 1st Lane 1 2886 Tree Ridge 1 3041 3rd Ave 1 6467 Best Ave 1 8811 Maple Hwy 1 6193 1st Hwy 1 9929 Rock Drive 1 2903 Weaver Drive 1 8907 Tree Ave 1 2820 Britain St 1 9580 MLK Ave 1 2201 4th Lane 1 9856 Apache St 1 6165 Rock Ridge 1 5260 Francis Drive 1 5480 3rd Ridge 1 4642 Rock Ridge 1 4188 Britain Ave 1 5838 Pine Lane 1 8621 Best Ridge 1 8906 Elm Lane 1 4585 Francis Ave 1 5279 Pine Ridge 1 5839 Weaver Lane 1 5586 2nd St 1 4254 Best Ridge 1 3053 Lincoln Drive 1 3397 5th Ave 1 1951 Best Ave 1 7535 5th Lane 1 8917 Cherokee Lane 1 1536 Flute Drive 1 1028 Sky Lane 1 2668 Cherokee St 1 6717 Best Drive 1 1806 Weaver Ridge 1 8477 Francis Hwy 1 9369 Flute Hwy 1 2696 Cherokee Ridge 1 3184 Oak Ave 1 3282 4th Lane 1 4964 Elm Lane 1 8404 Embaracadero St 1 7783 Lincoln Hwy 1 3553 Texas Ave 1 7928 Maple Ridge 1 6259 Weaver St 1 7380 5th Hwy 1 8689 Maple Hwy 1 8704 Britain Lane 1 5455 Oak Hwy 1 3039 Oak Hwy 1 7066 Texas Ave 1 5211 Weaver Drive 1 5540 Sky St 1 4910 1st Lane 1 3171 Andromedia Lane 1 7162 Maple Ave 1 6429 4th Hwy 1 5191 4th St 1 7534 MLK Hwy 1 5969 Francis St 1 4793 4th Ridge 1 6751 5th Hwy 1 8548 Cherokee Ridge 1 4907 Andromedia Drive 1 8949 Rock Hwy 1 1540 Apache Lane 1 6492 4th Lane 1 2509 Rock Drive 1 2654 Embaracadero St 1 5124 Maple St 1 1687 3rd Lane 1 2306 5th Lane 1 4369 Maple Lane 1 1273 Rock Lane 1 6955 Pine Drive 1 8983 Francis Ridge 1 3177 MLK Ridge 1 5584 Britain Lane 1 9278 Francis Ridge 1 1810 Elm Hwy 1 3488 Flute Lane 1 4154 Lincoln Hwy 1 2646 MLK Drive 1 9325 Lincoln Drive 1 8204 Pine Lane 1 5269 Flute Hwy 1 2539 Embaracadero Ridge 1 9286 Oak Ave 1 7502 Rock Lane 1 4755 1st St 1 9573 2nd Ave 1 1240 Tree Lane 1 6259 Lincoln Hwy 1 2318 Washington Hwy 1 3416 Washington Drive 1 5994 5th Ave 1 4905 Francis Ave 1 5649 Texas Ave 1 8940 Elm Ave 1 5904 1st Drive 1 5352 Lincoln Drive 1 3590 Best Hwy 1 8941 Solo Ridge 1 7460 Apache Lane 1 9169 Pine Ridge 1 7281 Oak St 1 8701 5th Lane 1 9138 1st St 1 3550 Washington Ave 1 2003 2nd Hwy 1 4434 Weaver St 1 4279 Solo Drive 1 2644 Elm Drive 1 2862 Tree Ridge 1 9109 Britain Drive 1 6456 Andromedia Drive 1 9816 Britain St 1 9879 Apache Drive 1 5189 Francis Drive 1 2914 Oak Drive 1 4876 Washington Drive 1 7426 Rock Drive 1 8973 Washington St 1 1832 Elm Hwy 1 3525 3rd Hwy 1 2654 Elm Drive 1 1679 2nd Hwy 1 4158 Washington Lane 1 3878 Tree Lane 1 3998 Flute St 1 5431 3rd Ridge 1 2889 Francis St 1 3790 Andromedia Hwy 1 7459 Flute St 1 8832 Pine Drive 1 5483 Francis Drive 1 5532 Francis Lane 1 9639 Britain Ridge 1 8995 1st Ave 1 7828 Cherokee Ave 1 2920 5th Ave 1 2100 MLK St 1 8509 Apache St 1 4629 Elm Ridge 1 3457 Texas Lane 1 1941 5th Ridge 1 8862 Maple Ridge 1 6149 Best Ridge 1 6317 Best St 1 4429 Washington St 1 4618 Flute Ave 1 2293 Washington Ave 1 2299 1st St 1 3797 Solo Lane 1 6848 Elm Hwy 1 1186 Rock St 1 5719 2nd Lane 1 1515 Pine Lane 1 7705 Best Ridge 1 2809 Francis Lane 1 8215 Flute Drive 1 3936 Tree Drive 1 1128 Maple Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
fraud_con['fraud_reported']=le.fit_transform(fraud_con['fraud_reported'])
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null int8 12 insured_hobbies 1000 non-null int8 13 insured_relationship 1000 non-null int8 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null int8 18 collision_type 1000 non-null int8 19 incident_severity 1000 non-null int8 20 authorities_contacted 1000 non-null int8 21 incident_state 1000 non-null int8 22 incident_city 1000 non-null int8 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null int8 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null int8 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(16), object(3) memory usage: 227.5+ KB
fraud_enc = fraud_con.copy()
fraud_enc=fraud_enc.drop('incident_location',axis=1)
fraud_enc.shape
(1000, 37)
fraud_enc.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null int8 12 insured_hobbies 1000 non-null int8 13 insured_relationship 1000 non-null int8 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null int8 18 collision_type 1000 non-null int8 19 incident_severity 1000 non-null int8 20 authorities_contacted 1000 non-null int8 21 incident_state 1000 non-null int8 22 incident_city 1000 non-null int8 23 incident_hour_of_the_day 1000 non-null int64 24 number_of_vehicles_involved 1000 non-null int64 25 property_damage 1000 non-null int8 26 bodily_injuries 1000 non-null int64 27 witnesses 1000 non-null int64 28 police_report_available 1000 non-null int8 29 total_claim_amount 999 non-null float64 30 injury_claim 1000 non-null int64 31 property_claim 994 non-null float64 32 vehicle_claim 1000 non-null int64 33 auto_make 1000 non-null int8 34 auto_model 1000 non-null int8 35 auto_year 1000 non-null int64 36 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(16), object(2) memory usage: 219.7+ KB
final_df = fraud_enc.drop(['policy_bind_date', 'incident_date','auto_model'], axis = 1)
final_df.columns
Index(['months_as_customer', 'age', 'policy_state', 'policy_csl',
'policy_deductable', 'policy_annual_premium', 'umbrella_limit',
'insured_zip', 'insured_sex', 'insured_education_level',
'insured_occupation', 'insured_hobbies', 'insured_relationship',
'capital-gains', 'capital-loss', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted', 'incident_state',
'incident_city', 'incident_hour_of_the_day',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'witnesses', 'police_report_available', 'total_claim_amount',
'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
'auto_year', 'fraud_reported'],
dtype='object')
final_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 34 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_state 1000 non-null int8 3 policy_csl 1000 non-null int8 4 policy_deductable 1000 non-null int64 5 policy_annual_premium 991 non-null float64 6 umbrella_limit 799 non-null float64 7 insured_zip 1000 non-null int64 8 insured_sex 1000 non-null int32 9 insured_education_level 1000 non-null int8 10 insured_occupation 1000 non-null int8 11 insured_hobbies 1000 non-null int8 12 insured_relationship 1000 non-null int8 13 capital-gains 1000 non-null int64 14 capital-loss 1000 non-null int64 15 incident_type 1000 non-null int8 16 collision_type 1000 non-null int8 17 incident_severity 1000 non-null int8 18 authorities_contacted 1000 non-null int8 19 incident_state 1000 non-null int8 20 incident_city 1000 non-null int8 21 incident_hour_of_the_day 1000 non-null int64 22 number_of_vehicles_involved 1000 non-null int64 23 property_damage 1000 non-null int8 24 bodily_injuries 1000 non-null int64 25 witnesses 1000 non-null int64 26 police_report_available 1000 non-null int8 27 total_claim_amount 999 non-null float64 28 injury_claim 1000 non-null int64 29 property_claim 994 non-null float64 30 vehicle_claim 1000 non-null int64 31 auto_make 1000 non-null int8 32 auto_year 1000 non-null int64 33 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(15) memory usage: 203.1 KB
from sklearn.impute import KNNImputer
imp = KNNImputer(n_neighbors=10)
fraud_imp = pd.DataFrame(imp.fit_transform(final_df),columns=final_df.columns)
fraud_imp.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 34 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null float64 1 age 1000 non-null float64 2 policy_state 1000 non-null float64 3 policy_csl 1000 non-null float64 4 policy_deductable 1000 non-null float64 5 policy_annual_premium 1000 non-null float64 6 umbrella_limit 1000 non-null float64 7 insured_zip 1000 non-null float64 8 insured_sex 1000 non-null float64 9 insured_education_level 1000 non-null float64 10 insured_occupation 1000 non-null float64 11 insured_hobbies 1000 non-null float64 12 insured_relationship 1000 non-null float64 13 capital-gains 1000 non-null float64 14 capital-loss 1000 non-null float64 15 incident_type 1000 non-null float64 16 collision_type 1000 non-null float64 17 incident_severity 1000 non-null float64 18 authorities_contacted 1000 non-null float64 19 incident_state 1000 non-null float64 20 incident_city 1000 non-null float64 21 incident_hour_of_the_day 1000 non-null float64 22 number_of_vehicles_involved 1000 non-null float64 23 property_damage 1000 non-null float64 24 bodily_injuries 1000 non-null float64 25 witnesses 1000 non-null float64 26 police_report_available 1000 non-null float64 27 total_claim_amount 1000 non-null float64 28 injury_claim 1000 non-null float64 29 property_claim 1000 non-null float64 30 vehicle_claim 1000 non-null float64 31 auto_make 1000 non-null float64 32 auto_year 1000 non-null float64 33 fraud_reported 1000 non-null float64 dtypes: float64(34) memory usage: 265.8 KB
X = fraud_imp.drop('fraud_reported',axis=1)
Y = fraud_imp['fraud_reported']
Y.value_counts()
0.0 753 1.0 247 Name: fraud_reported, dtype: int64
X.shape
(1000, 33)
X.drop(['policy_state', 'policy_deductable', 'umbrella_limit',
'insured_zip', 'insured_hobbies','capital-gains', 'capital-loss', 'incident_state',
'incident_city', 'incident_hour_of_the_day',
'witnesses','auto_year'],axis=1,inplace=True)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
scaled_pred = pd.DataFrame(sc.fit_transform(X),columns=X.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(scaled_pred, Y, test_size = 0.2, random_state = 0)
x_train.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_train.shape
(800, 21)
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train,y_train)
pred_model = model_1.predict(x_test)
print("Training Accuracy: ", model_1.score(x_train, y_train))
print('Testing Accuarcy: ', model_1.score(x_test, y_test))
Training Accuracy: 0.7975 Testing Accuarcy: 0.75
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test, pred_model)
print(cr)
precision recall f1-score support
0.0 0.77 0.92 0.84 143
1.0 0.62 0.32 0.42 57
accuracy 0.75 200
macro avg 0.70 0.62 0.63 200
weighted avg 0.73 0.75 0.72 200
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train,y_train)
rf_pred_model = rf_model.predict(x_test)
print("Training Accuracy: ", rf_model.score(x_train, y_train))
print('Testing Accuarcy: ', rf_model.score(x_test, y_test))
Training Accuracy: 1.0 Testing Accuarcy: 0.78
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.79 0.94 0.86 143
1.0 0.72 0.37 0.49 57
accuracy 0.78 200
macro avg 0.76 0.66 0.67 200
weighted avg 0.77 0.78 0.75 200
from imblearn.over_sampling import SMOTE
oversample = SMOTE()
X_res, y_res = oversample.fit_resample(X, Y)
from sklearn.model_selection import train_test_split
x_train_smote, x_test_smote, y_train_smote, y_test_smote = train_test_split(X_res, y_res, test_size = 0.2, random_state = 0)
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train_smote,y_train_smote)
pred_model = model_1.predict(x_test_smote)
print("Training Accuracy: ", model_1.score(x_train_smote, y_train_smote))
print('Testing Accuarcy: ', model_1.score(x_test_smote, y_test_smote))
Training Accuracy: 0.6079734219269103 Testing Accuarcy: 0.6059602649006622
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote, pred_model)
print(cr)
precision recall f1-score support
0.0 0.64 0.53 0.58 154
1.0 0.58 0.69 0.63 148
accuracy 0.61 302
macro avg 0.61 0.61 0.60 302
weighted avg 0.61 0.61 0.60 302
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train_smote,y_train_smote)
rf_pred_model = rf_model.predict(x_test_smote)
print("Training Accuracy: ", rf_model.score(x_train_smote, y_train_smote))
print('Testing Accuarcy: ', rf_model.score(x_test_smote, y_test_smote))
Training Accuracy: 1.0 Testing Accuarcy: 0.847682119205298
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.86 0.84 0.85 154
1.0 0.84 0.86 0.85 148
accuracy 0.85 302
macro avg 0.85 0.85 0.85 302
weighted avg 0.85 0.85 0.85 302
import pickle
filename = 'final_model.pkl'
pickle.dump(rf_model, open(filename, 'wb'))
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(x_test, y_test)
print(result)
0.495
fraud_con.columns
Index(['months_as_customer', 'age', 'policy_bind_date', 'policy_state',
'policy_csl', 'policy_deductable', 'policy_annual_premium',
'umbrella_limit', 'insured_zip', 'insured_sex',
'insured_education_level', 'insured_occupation', 'insured_hobbies',
'insured_relationship', 'capital-gains', 'capital-loss',
'incident_date', 'incident_type', 'collision_type', 'incident_severity',
'authorities_contacted', 'incident_state', 'incident_city',
'incident_location', 'incident_hour_of_the_day',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'witnesses', 'police_report_available', 'total_claim_amount',
'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
'auto_model', 'auto_year', 'fraud_reported'],
dtype='object')
X_res.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_res=X_res.copy()
# # x_res.drop(['policy_state','policy_csl','umbrella_limit','insured_zip', 'insured_sex', 'insured_education_level','insured_relationship',
# 'capital-gains', 'capital-loss', 'incident_type','number_of_vehicles_involved','bodily_injuries',
# 'witnesses'],axis=1)
# x_res.drop(['months_as_customer', 'policy_state', 'policy_csl',
# 'policy_deductable', 'policy_annual_premium', 'umbrella_limit',
# 'insured_zip', 'insured_hobbies',
# 'capital-gains', 'capital-loss', 'incident_state',
# 'incident_city', 'incident_hour_of_the_day',
# 'number_of_vehicles_involved', 'bodily_injuries',
# 'witnesses', 'total_claim_amount',
# 'injury_claim'],axis=1,inplace=True)
x_res.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_res.shape
(1506, 21)
from sklearn.model_selection import train_test_split
x_train_smote_upd, x_test_smote_upd, y_train_smote_upd, y_test_smote_upd = train_test_split(x_res, y_res, test_size = 0.2, random_state = 0)
from xgboost import XGBClassifier
model=XGBClassifier(use_label_encoder=False)
xgbmodel_clf = model.fit(x_train_smote_upd,y_train_smote_upd)
print(xgbmodel_clf)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.300000012, max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
tree_method='exact', use_label_encoder=False,
validate_parameters=1, verbosity=None)
pred_xbgclf_model = model.predict(x_test_smote_upd)
print("Training Accuracy: ", xgbmodel_clf.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', xgbmodel_clf.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 1.0 Testing Accuarcy: 0.8211920529801324
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, pred_xbgclf_model)
print(cr)
precision recall f1-score support
0.0 0.81 0.84 0.83 154
1.0 0.83 0.80 0.81 148
accuracy 0.82 302
macro avg 0.82 0.82 0.82 302
weighted avg 0.82 0.82 0.82 302
import pickle
filename = 'final_model.pkl'
pickle.dump(xgbmodel_clf, open(filename, 'wb'))
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train_smote_upd,y_train_smote_upd)
pred_model = model_1.predict(x_test_smote_upd)
print("Training Accuracy: ", model_1.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', model_1.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 0.6079734219269103 Testing Accuarcy: 0.6059602649006622
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, pred_model)
print(cr)
precision recall f1-score support
0.0 0.64 0.53 0.58 154
1.0 0.58 0.69 0.63 148
accuracy 0.61 302
macro avg 0.61 0.61 0.60 302
weighted avg 0.61 0.61 0.60 302
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train_smote_upd,y_train_smote_upd)
rf_pred_model = rf_model.predict(x_test_smote_upd)
print("Training Accuracy: ", rf_model.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', rf_model.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 1.0 Testing Accuarcy: 0.8675496688741722
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.88 0.86 0.87 154
1.0 0.86 0.88 0.87 148
accuracy 0.87 302
macro avg 0.87 0.87 0.87 302
weighted avg 0.87 0.87 0.87 302
fraud_con.police_report_available.value_counts()
1 343 0 343 2 314 Name: police_report_available, dtype: int64
import pickle
loaded_model = pickle.load(open('final_model.pkl', 'rb'))
result = loaded_model.score(x_test_smote_upd, y_test_smote_upd)
print(result)
0.8211920529801324
import gradio as gr
def encode_age(df): # Binning ages
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
categories = pd.cut(df.Age, bins, labels=False)
df.Age = categories
return df
def encode_education(df):
mapping = {"JD":3,"High School":2,"Associate":0,"MD":4,"Masters":5,"PhD":6,"College":1}
return df.replace({'Education': mapping})
def encode_incident_type(df):
mapping = {"Multi-vehicle Collision":0,"Single Vehicle Collision":2,"Vehicle Theft":3,"Parked Car":1}
return df.replace({'Incident Type': mapping})
def encode_relationship(df):
mapping = {"own-child":3,"other-relative":2,"not-in-family":1,"husband":0,"wife":5,"unmarried":4}
return df.replace({'Relationship': mapping})
def encode_occupation(df):
mapping = {"machine-op-inspct":6,"prof-specialty":9,"tech-support":12,"sales":11,"exec-managerial":3,"craft-repair":2,"transport-moving":13,"other-service":8,"priv-house-serv":7,"armed-forces":1,"adm-clerical":0,"protective-serv":10,"handlers-cleaners":5,"farming-fishing":4}
return df.replace({'Occupation': mapping})
def encode_state(df):
mapping = {"OH":2,"IL":0,"IN": 1}
return df.replace({'State of Policy': mapping})
def encode_coll_type(df):
mapping = {"Rear Collision":1,"Side Collision":2,"Front Collision":0,"Unknown":3}
return df.replace({'Collision Type': mapping})
def encode_incident_severity(df):
mapping = {"Minor Damage":1,"Total Loss":2,"Major Damage":0,"Trivial Damage":3}
return df.replace({'Severity': mapping})
def encode_policy_csl(df):
mapping = {"250/500":1,"100/300":0,"500/1000":2}
return df.replace({'Policy CSL': mapping})
def encode_property_damage(df):
mapping = {"Unknown":1,"NO":0,"YES":2}
return df.replace({'Property Damage': mapping})
def encode_police_rep(df):
mapping = {"Unknown":1,"NO":0,"YES":2}
return df.replace({'Police_Report_Available': mapping})
def encode_auth_con(df):
mapping = {"Police":4,"Fire":1,"Other":3,"Ambulance":0,"None":2}
return df.replace({'Authorities': mapping})
def encode_sex(df):
mapping = {"male": 0, "female": 1}
return df.replace({'Gender': mapping})
def encode_auto_make(df):
mapping = {"Dodge":11,"Suburu":10,"Saab":4,"Nissan":9,"Chevrolet":3,"Ford":5,"BMW":2,"Toyota":12,"Audi":1,"Accura":13,"Volkswagen":0,"Jeep":7,"Mercedes":8,"Honda":6}
return df.replace({'Auto Make': mapping})
from sklearn.preprocessing import StandardScaler
st = StandardScaler()
def predict_fraud(months_as_customer,age,sex, insured_education_level,insured_occupation, insured_relationship,incident_type, collision_type,incident_severity,number_of_vehicles_involved, authorities_contacted, bodily_injuries,property_damage, police_report_available, auto_make,policy_csl,policy_annual_premium,total_claim_amount,injury_claim,property_claim,vehicle_claim):
df = pd.DataFrame.from_dict({'Months':[months_as_customer],'Age':[age],'Gender':[sex],'Education':[insured_education_level],'Occupation':[insured_occupation],'Relationship':[insured_relationship],'Incident Type':[incident_type], 'Collision Type':[collision_type],'Severity':[incident_severity],'Vehicles Involved':[number_of_vehicles_involved], 'Authorities':[authorities_contacted],'Injuries':[bodily_injuries], 'Property Damage':[property_damage], 'Police_Report_Available':[police_report_available],'Auto Make':[auto_make],'Policy CSL':[policy_csl],'Annual Premium':[policy_annual_premium],'Total Claim':[total_claim_amount],'Injury Claim Amount':[injury_claim],'Property Claim Amount':[property_claim],'Vehicle Claim':[vehicle_claim]})
df = encode_sex(df)
df = encode_education(df)
df = encode_auto_make(df)
df = encode_relationship(df)
df = encode_age(df)
df = encode_policy_csl(df)
df = encode_occupation(df)
df = encode_incident_type(df)
df = encode_incident_severity(df)
df = encode_coll_type(df)
df = encode_property_damage(df)
df = encode_police_rep(df)
df = encode_auth_con(df)
df = sc.transform(df)
pred = loaded_model.predict_proba(df)[0]
res = {'Not Fraud': float(pred[0]), 'Fraud': float(pred[1])}
return res
months_as_customer = gr.inputs.Number(label="Months")
age = gr.inputs.Slider(minimum=0, maximum=120, default=22, label="Age")
policy_deductable = gr.inputs.Number(label="Deduction")
policy_annual_premium = gr.inputs.Number(label="Annual Premium")
#umbrella_limit = gr.inputs.Number(label="Limit")
insured_education_level = gr.inputs.Dropdown(['JD','High School','Associate','MD','Masters','PhD','College'],label="Education")
insured_occupation = gr.inputs.Dropdown(['machine-op-inspct','prof-specialty','tech-support','sales','exec-managerial','craft-repair','transport-moving','other-service','priv-house-serv','armed-forces','adm-clerical','protective-serv','handlers-cleaners','farming-fishing'],label="Occupation")
auto_make = gr.inputs.Dropdown(['Dodge','Suburu','Saab','Nissan','Chevrolet','Ford','BMW','Toyota','Audi','Accura','Volkswagen','Jeep','Mercedes','Honda'],label="Auto Make")
sex = gr.inputs.Radio(['female', 'male'], label="Gender")
policy_csl = gr.inputs.Radio(['250/500','100/300','500/1000'],label='Polcy CSL')
#capital-gains = gr.inputs.Number(label="Gain")
#capital-loss = gr.inputs.Number(label="Loss")
incident_type = gr.inputs.Dropdown(['Multi-vehicle Collision','Single Vehicle Collision','Vehicle Theft','Parked Car'],label="Incident Type")
insured_relationship = gr.inputs.Dropdown(['own-child','other-relative','not-in-family','husband','wife','unmarried'],label="Relationship")
incident_severity = gr.inputs.Dropdown(['Minor Damage','Total Loss','Major Damage','Trivial Damage'],label="Severity")
collision_type = gr.inputs.Dropdown(['Rear Collision','Side Collision','Front Collision','Unknown'],label="Collision Type")
number_of_vehicles_involved = gr.inputs.Number(label='Vehicles Involved')
property_damage = gr.inputs.Dropdown(['Unknown','NO','YES'],label="Property Damage")
police_report_available = gr.inputs.Dropdown(['Unknown','NO','YES'],label="Police_Report_Available")
authorities_contacted = gr.inputs.Dropdown(['Police','Fire','Other','Ambulance','None'],label="Authorities")
total_claim_amount = gr.inputs.Number(label='Total Claim')
#witnesses = gr.inputs.Number(label='Witnesses')
bodily_injuries = gr.inputs.Number(label='Injuries')
injury_claim = gr.inputs.Number(label='Injury Claim Amount')
property_claim = gr.inputs.Number(label='Property Claim Amount')
vehicle_claim = gr.inputs.Number(label='Vehicle Claim')
gr.Interface(predict_fraud, [months_as_customer,age,sex, insured_education_level,insured_occupation, insured_relationship,incident_type, collision_type,incident_severity,number_of_vehicles_involved, authorities_contacted, bodily_injuries,property_damage, police_report_available, auto_make,policy_csl,policy_annual_premium,total_claim_amount,injury_claim,property_claim,vehicle_claim], "label").launch(share=True)
Running on local URL: http://127.0.0.1:7865/ Running on public URL: https://34886.gradio.app This share link will expire in 72 hours. To get longer links, send an email to: support@gradio.app
(<Flask 'gradio.networking'>, 'http://127.0.0.1:7865/', 'https://34886.gradio.app')
Public access to the demo app is https://34886.gradio.app
from interpret.glassbox import ExplainableBoostingClassifier
ebm = ExplainableBoostingClassifier()
ebm.fit(x_train_smote_upd,y_train_smote_upd)
ExplainableBoostingClassifier(feature_names=['months_as_customer', 'age',
'policy_csl',
'policy_annual_premium',
'insured_sex',
'insured_education_level',
'insured_occupation',
'insured_relationship',
'incident_type', 'collision_type',
'incident_severity',
'authorities_contacted',
'number_of_vehicles_involved',
'property_damage',
'bodily_injuries',
'police_report...
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction', ...])
from interpret import show
ebm_global = ebm.explain_global()
show(ebm_global)
<iframe src="http://127.0.0.1:7001/2728157030480/" width=100% height=800 frameBorder="0"></iframe>
ebm_local = ebm.explain_local(x_test_smote_upd, y_test_smote_upd)
show(ebm_local)
<iframe src="http://127.0.0.1:7001/2728156053456/" width=100% height=800 frameBorder="0"></iframe>
This is run on balanced dataset using SMOTE technique
from flaml import AutoML
automl = AutoML()
automl.fit(x_train_smote_upd,y_train_smote_upd, task="classification")
[flaml.automl: 11-18 16:31:43] {1431} INFO - Evaluation method: cv
[flaml.automl: 11-18 16:31:43] {1477} INFO - Minimizing error metric: 1-roc_auc
[flaml.automl: 11-18 16:31:43] {1514} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']
[flaml.automl: 11-18 16:31:43] {1746} INFO - iteration 0, current learner lgbm
[flaml.automl: 11-18 16:31:43] {1925} INFO - at 0.2s, best lgbm's error=0.1478, best lgbm's error=0.1478
[flaml.automl: 11-18 16:31:43] {1746} INFO - iteration 1, current learner lgbm
[flaml.automl: 11-18 16:31:43] {1925} INFO - at 0.2s, best lgbm's error=0.1240, best lgbm's error=0.1240
[flaml.automl: 11-18 16:31:43] {1746} INFO - iteration 2, current learner lgbm
[flaml.automl: 11-18 16:31:43] {1925} INFO - at 0.3s, best lgbm's error=0.1240, best lgbm's error=0.1240
[flaml.automl: 11-18 16:31:43] {1746} INFO - iteration 3, current learner lgbm
[flaml.automl: 11-18 16:31:43] {1925} INFO - at 0.4s, best lgbm's error=0.1016, best lgbm's error=0.1016
[flaml.automl: 11-18 16:31:43] {1746} INFO - iteration 4, current learner lgbm
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 0.6s, best lgbm's error=0.1016, best lgbm's error=0.1016
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 5, current learner lgbm
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 0.8s, best lgbm's error=0.0794, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 6, current learner xgboost
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 0.9s, best xgboost's error=0.1470, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 7, current learner lgbm
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 1.1s, best lgbm's error=0.0794, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 8, current learner lgbm
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 1.2s, best lgbm's error=0.0794, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 9, current learner xgboost
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 1.3s, best xgboost's error=0.1283, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 10, current learner extra_tree
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 1.4s, best extra_tree's error=0.1777, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 11, current learner xgboost
[flaml.automl: 11-18 16:31:44] {1925} INFO - at 1.5s, best xgboost's error=0.1283, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:44] {1746} INFO - iteration 12, current learner extra_tree
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 1.7s, best extra_tree's error=0.1400, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 13, current learner rf
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 1.8s, best rf's error=0.1456, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 14, current learner rf
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 1.9s, best rf's error=0.1100, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 15, current learner rf
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.0s, best rf's error=0.1100, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 16, current learner lgbm
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.1s, best lgbm's error=0.0794, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 17, current learner extra_tree
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.2s, best extra_tree's error=0.1400, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 18, current learner lgbm
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.2s, best lgbm's error=0.0794, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 19, current learner rf
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.4s, best rf's error=0.1100, best lgbm's error=0.0794
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 20, current learner lgbm
[flaml.automl: 11-18 16:31:45] {1925} INFO - at 2.5s, best lgbm's error=0.0640, best lgbm's error=0.0640
[flaml.automl: 11-18 16:31:45] {1746} INFO - iteration 21, current learner rf
[flaml.automl: 11-18 16:31:46] {1925} INFO - at 2.6s, best rf's error=0.1064, best lgbm's error=0.0640
[flaml.automl: 11-18 16:31:46] {1746} INFO - iteration 22, current learner lgbm
[flaml.automl: 11-18 16:31:46] {1925} INFO - at 2.7s, best lgbm's error=0.0626, best lgbm's error=0.0626
[flaml.automl: 11-18 16:31:46] {1746} INFO - iteration 23, current learner lgbm
[flaml.automl: 11-18 16:31:46] {1925} INFO - at 2.9s, best lgbm's error=0.0626, best lgbm's error=0.0626
[flaml.automl: 11-18 16:31:46] {1746} INFO - iteration 24, current learner lgbm
[flaml.automl: 11-18 16:31:46] {1925} INFO - at 3.0s, best lgbm's error=0.0626, best lgbm's error=0.0626
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[flaml.automl: 11-18 16:32:36] {1925} INFO - at 53.2s, best rf's error=0.0716, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:36] {1746} INFO - iteration 119, current learner lgbm
[flaml.automl: 11-18 16:32:37] {1925} INFO - at 54.5s, best lgbm's error=0.0541, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:37] {1746} INFO - iteration 120, current learner rf
[flaml.automl: 11-18 16:32:38] {1925} INFO - at 54.9s, best rf's error=0.0716, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:38] {1746} INFO - iteration 121, current learner rf
[flaml.automl: 11-18 16:32:38] {1925} INFO - at 55.2s, best rf's error=0.0716, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:38] {1746} INFO - iteration 122, current learner xgboost
[flaml.automl: 11-18 16:32:38] {1925} INFO - at 55.3s, best xgboost's error=0.1182, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:38] {1746} INFO - iteration 123, current learner rf
[flaml.automl: 11-18 16:32:39] {1925} INFO - at 55.7s, best rf's error=0.0716, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:39] {1746} INFO - iteration 124, current learner lgbm
[flaml.automl: 11-18 16:32:44] {1925} INFO - at 61.4s, best lgbm's error=0.0541, best lgbm's error=0.0541
[flaml.automl: 11-18 16:32:44] {2032} INFO - selected model: LGBMClassifier(colsample_bytree=0.2835986500135067,
learning_rate=0.022972887058962492, max_bin=512,
min_child_samples=8, n_estimators=573, num_leaves=113,
objective='binary', reg_alpha=0.003564379949322195,
reg_lambda=0.0009765625, verbose=-1)
[flaml.automl: 11-18 16:32:46] {2093} INFO - retrain lgbm for 1.2s
[flaml.automl: 11-18 16:32:46] {2099} INFO - retrained model: LGBMClassifier(colsample_bytree=0.2835986500135067,
learning_rate=0.022972887058962492, max_bin=512,
min_child_samples=8, n_estimators=573, num_leaves=113,
objective='binary', reg_alpha=0.003564379949322195,
reg_lambda=0.0009765625, verbose=-1)
[flaml.automl: 11-18 16:32:46] {1538} INFO - fit succeeded
[flaml.automl: 11-18 16:32:46] {1539} INFO - Time taken to find the best model: 31.9897563457489
print('Best ML leaner:', automl.best_estimator)
print('Best hyperparmeter config:', automl.best_config)
print('Best accuracy on validation data: {0:.4g}'.format(1-automl.best_loss))
print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))
Best ML leaner: lgbm
Best hyperparmeter config: {'n_estimators': 573, 'num_leaves': 113, 'min_child_samples': 8, 'learning_rate': 0.022972887058962492, 'log_max_bin': 10, 'colsample_bytree': 0.2835986500135067, 'reg_alpha': 0.003564379949322195, 'reg_lambda': 0.0009765625}
Best accuracy on validation data: 0.9459
Training duration of best run: 3.511 s